Persistent network model diagnostics - balanced statistics

This file shows diagnostics for persistent network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")
library("here")
## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMP
load(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.bal.rda"))

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
edges 2017.5 2017.5 2017.5 2017.5 2017.5 2017.5 2017.5 2017.5
nodefactor.deg.main.1 NA NA NA 1699.0 1699.0 1699.0 1699.0 1699.0
nodefactor.race..wa.B NA 285.5 285.5 285.5 285.5 285.5 285.5 285.5
nodefactor.race..wa.H NA 605.3 605.3 605.3 605.3 605.3 605.3 605.3
nodefactor.region.EW NA NA NA NA 367.6 367.6 367.6 367.6
nodefactor.region.OW NA NA NA NA 1182.3 1182.3 1182.3 1182.3
concurrent NA NA NA NA NA NA 1384.0 1384.0
nodematch.race..wa.B NA NA 8.5 8.5 8.5 8.5 8.5 8.5
nodematch.race..wa.H NA NA 51.2 51.2 51.2 51.2 51.2 51.2
nodematch.race..wa.O NA NA 1247.1 1247.1 1247.1 1247.1 1247.1 1247.1
nodematch.region NA NA NA NA NA NA NA 1614.0
absdiff.sqrt.age NA NA NA NA NA 1664.8 1664.8 1664.8
degrange 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
nodematch.role.class.I -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf

The control settings for these models are:

set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
                                 MCMC.samplesize = 7500,
                                 MCMC.burnin = 1e+6,
                                 MPLE.max.dyad.types = 1e+7,
                                 init.method = "zeros",
                                 MCMLE.maxit = 400,
                                 parallel = np/2,
                                 parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##           Mean             SD       Naive SE Time-series SE 
##        -0.2693        40.4159         0.2333         0.2324 
## 
## 2. Quantiles for each variable:
## 
##  2.5%   25%   50%   75% 97.5% 
## -78.5 -27.5  -0.5  26.5  78.5 
## 
## 
## Sample statistics cross-correlations:
##       edges
## edges     1
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges
## Lag 0      1.00000000
## Lag 1e+05 -0.01264310
## Lag 2e+05  0.02340828
## Lag 3e+05 -0.00426963
## Lag 4e+05 -0.01833374
## Lag 5e+05 -0.02891705
## Chain 2 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05  0.0097286168
## Lag 2e+05 -0.0004894332
## Lag 3e+05  0.0142298259
## Lag 4e+05  0.0077479447
## Lag 5e+05  0.0153968890
## Chain 3 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.017667766
## Lag 2e+05  0.013800249
## Lag 3e+05  0.005901856
## Lag 4e+05  0.002580839
## Lag 5e+05  0.003736537
## Chain 4 
##                 edges
## Lag 0      1.00000000
## Lag 1e+05  0.01953609
## Lag 2e+05  0.01789521
## Lag 3e+05 -0.01244918
## Lag 4e+05 -0.02167088
## Lag 5e+05  0.01275054
## Chain 5 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05 -0.0055063925
## Lag 2e+05  0.0083421464
## Lag 3e+05 -0.0131939117
## Lag 4e+05 -0.0001138946
## Lag 5e+05  0.0158752099
## Chain 6 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.018427988
## Lag 2e+05  0.008982739
## Lag 3e+05 -0.030548372
## Lag 4e+05  0.018199035
## Lag 5e+05 -0.006587675
## Chain 7 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.034409587
## Lag 2e+05  0.017008519
## Lag 3e+05 -0.016041825
## Lag 4e+05 -0.006265774
## Lag 5e+05  0.014039680
## Chain 8 
##                 edges
## Lag 0      1.00000000
## Lag 1e+05  0.01156856
## Lag 2e+05  0.02465699
## Lag 3e+05  0.01027490
## Lag 4e+05 -0.01866472
## Lag 5e+05  0.00814208
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.2032 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.8389906 
## Joint P-value (lower = worse):  0.8345032 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.4927 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.6222378 
## Joint P-value (lower = worse):  0.6365891 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.3314 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.7403632 
## Joint P-value (lower = worse):  0.7407062 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.0489 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.9609959 
## Joint P-value (lower = worse):  0.9610362 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.481 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.1385718 
## Joint P-value (lower = worse):  0.1321135 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.746 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.0808636 
## Joint P-value (lower = worse):  0.08687598 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.6178 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.5367057 
## Joint P-value (lower = worse):  0.511578 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.1803 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.8569124 
## Joint P-value (lower = worse):  0.8625581 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean    SD Naive SE Time-series SE
## edges                 -0.09110 39.98  0.23084        0.22820
## nodefactor.race..wa.B  0.07903 16.15  0.09322        0.09365
## nodefactor.race..wa.H  0.68867 23.45  0.13537        0.13636
## 
## 2. Quantiles for each variable:
## 
##                         2.5%    25%     50%   75% 97.5%
## edges                 -78.50 -26.50 -0.5000 26.50 78.50
## nodefactor.race..wa.B -31.52 -10.52  0.4832 10.48 32.48
## nodefactor.race..wa.H -44.34 -15.34  0.6600 16.66 46.66
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.34043850
## nodefactor.race..wa.B 0.3404385            1.00000000
## nodefactor.race..wa.H 0.4714240            0.07104756
##                       nodefactor.race..wa.H
## edges                            0.47142397
## nodefactor.race..wa.B            0.07104756
## nodefactor.race..wa.H            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.014056138           0.002684782          -0.009837923
## Lag 2e+05  0.009601482           0.027872754          -0.006658632
## Lag 3e+05 -0.021384550           0.016004081          -0.003078447
## Lag 4e+05  0.020746657           0.011123528           0.018909300
## Lag 5e+05 -0.031688277           0.021454748           0.006354803
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.015375920           0.013907793           0.005285046
## Lag 2e+05  0.004422912           0.010542225          -0.010488125
## Lag 3e+05  0.008273100          -0.021453347          -0.003996662
## Lag 4e+05 -0.020135105           0.014140349          -0.018196243
## Lag 5e+05  0.016189235          -0.009558596           0.002314518
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.008418285           0.020811394           -0.02164845
## Lag 2e+05  0.007272971           0.014815020           -0.01199940
## Lag 3e+05 -0.011430331           0.006676682            0.02394959
## Lag 4e+05  0.013043680          -0.026303588            0.01219512
## Lag 5e+05 -0.008226237          -0.019227916           -0.02001891
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.001047007           0.007165719           0.004493936
## Lag 2e+05 -0.002856752          -0.004022528           0.011372678
## Lag 3e+05 -0.004367930           0.016923844          -0.012796269
## Lag 4e+05 -0.015947214          -0.004892327           0.001331095
## Lag 5e+05 -0.019503067          -0.016475032           0.022703284
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.007100868           0.036214870           0.006731455
## Lag 2e+05  0.023051907           0.013640057           0.019028848
## Lag 3e+05 -0.016022507          -0.002459983          -0.017163470
## Lag 4e+05 -0.003299190           0.019133762          -0.026224077
## Lag 5e+05  0.008201155          -0.015749485           0.040439013
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.008263184           0.001980644           0.010036553
## Lag 2e+05 -0.008049266          -0.014203663           0.019661007
## Lag 3e+05  0.002443602           0.008704505           0.016815173
## Lag 4e+05 -0.036731424          -0.011608567           0.001098624
## Lag 5e+05  0.021488107          -0.006153972           0.007991003
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.004780772           0.006810960          -0.016046380
## Lag 2e+05 -0.001116000          -0.004969817          -0.012715600
## Lag 3e+05 -0.006197987          -0.019398210           0.002192296
## Lag 4e+05 -0.027419983          -0.002447443          -0.007998861
## Lag 5e+05  0.005475253           0.005611519          -0.004733637
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.025949984           0.020703104          -0.004187630
## Lag 2e+05 -0.009835624           0.010510133          -0.002304295
## Lag 3e+05 -0.006874789           0.004327962          -0.002507147
## Lag 4e+05 -0.021181794           0.009414561          -0.036419091
## Lag 5e+05  0.033435427           0.041390937           0.014618093
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.6982               -0.0121               -0.9571 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4850279             0.9903456             0.3385000 
## Joint P-value (lower = worse):  0.790269 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.0060                0.8227               -0.2323 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3143990             0.4106945             0.8162875 
## Joint P-value (lower = worse):  0.6425545 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.7328               -0.6441               -0.3608 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4636517             0.5194926             0.7182590 
## Joint P-value (lower = worse):  0.8784429 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.03504               0.79803              -0.75937 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9720451             0.4248510             0.4476300 
## Joint P-value (lower = worse):  0.7355735 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.07216              -0.62653              -0.43441 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9424724             0.5309689             0.6639910 
## Joint P-value (lower = worse):  0.8647927 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4155                0.6445                0.2857 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6777416             0.5192281             0.7751256 
## Joint P-value (lower = worse):  0.929725 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.2834                0.1738               -1.6865 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.19936280            0.86201320            0.09170284 
## Joint P-value (lower = worse):  0.2793509 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -2.6602               -0.8513               -1.4327 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##           0.007810195           0.394610098           0.151944926 
## Joint P-value (lower = worse):  0.08011385 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -0.49160 40.392  0.23320        0.22875
## nodefactor.race..wa.B  0.10247 16.100  0.09295        0.09439
## nodefactor.race..wa.H -0.64150 23.741  0.13707        0.13574
## nodematch.race..wa.B   0.03895  2.903  0.01676        0.01672
## nodematch.race..wa.H  -0.08816  6.900  0.03984        0.03983
## nodematch.race..wa.O  -0.01568 32.784  0.18928        0.18931
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%      50%    75% 97.5%
## edges                 -78.50 -27.500 -0.50000 26.500 79.50
## nodefactor.race..wa.B -30.52 -10.517 -0.51680 10.483 32.48
## nodefactor.race..wa.H -46.34 -17.340 -0.34000 15.660 45.66
## nodematch.race..wa.B   -5.48  -2.480 -0.47985  1.520  6.52
## nodematch.race..wa.H  -13.18  -5.181 -0.18150  4.819 13.82
## nodematch.race..wa.O  -63.08 -22.081 -0.08078 21.919 64.92
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.35032349
## nodefactor.race..wa.B 0.3503235            1.00000000
## nodefactor.race..wa.H 0.4731012            0.10951385
## nodematch.race..wa.B  0.0510416            0.31164161
## nodematch.race..wa.H  0.1251259           -0.01310216
## nodematch.race..wa.O  0.7840966           -0.01465899
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                            0.47310122          0.051041602
## nodefactor.race..wa.B            0.10951385          0.311641607
## nodefactor.race..wa.H            1.00000000         -0.021040693
## nodematch.race..wa.B            -0.02104069          1.000000000
## nodematch.race..wa.H             0.49246973         -0.005351267
## nodematch.race..wa.O            -0.03079702          0.007037903
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.125125858          0.784096612
## nodefactor.race..wa.B         -0.013102161         -0.014658986
## nodefactor.race..wa.H          0.492469731         -0.030797024
## nodematch.race..wa.B          -0.005351267          0.007037903
## nodematch.race..wa.H           1.000000000          0.004971174
## nodematch.race..wa.O           0.004971174          1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.004194474          0.0075914377          -0.012642645
## Lag 2e+05  0.001189938         -0.0077760006          -0.013051100
## Lag 3e+05 -0.013498475         -0.0015569134          -0.009593246
## Lag 4e+05 -0.028407617         -0.0002640455          -0.004609579
## Lag 5e+05  0.023342365          0.0074494724           0.020201513
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.017936373         -0.004130326         -0.001240103
## Lag 2e+05         -0.011342263         -0.007115653          0.011922060
## Lag 3e+05          0.008160085         -0.028034668          0.004731922
## Lag 4e+05          0.003387611          0.013319112         -0.008112927
## Lag 5e+05          0.000192203          0.001983806          0.035126530
## Chain 2 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.0155963242           0.005599207         -0.0065676747
## Lag 2e+05 -0.0056243883           0.007062517          0.0020552492
## Lag 3e+05 -0.0033827699          -0.005364336         -0.0172668074
## Lag 4e+05 -0.0222674387           0.030549916          0.0005323911
## Lag 5e+05  0.0007441259          -0.005040395          0.0069328232
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05          -0.01325389          0.021283659         -0.002475173
## Lag 2e+05           0.01487761          0.022805977         -0.013027309
## Lag 3e+05           0.02235377         -0.006504199          0.027756411
## Lag 4e+05          -0.04650066         -0.005498602         -0.013712560
## Lag 5e+05           0.01115581         -0.002193962         -0.004388494
## Chain 3 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000          1.0000000000            1.00000000
## Lag 1e+05  0.0011042129         -0.0034361775           -0.02295850
## Lag 2e+05  0.0003558665         -0.0135148853            0.01457583
## Lag 3e+05  0.0257859960          0.0067996675           -0.03581431
## Lag 4e+05  0.0221185423          0.0002173176            0.02190516
## Lag 5e+05 -0.0159792426          0.0067314282           -0.03160936
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000         1.0000000000          1.000000000
## Lag 1e+05           0.00184724        -0.0154134135         -0.012907295
## Lag 2e+05          -0.01540586         0.0055993066          0.002586412
## Lag 3e+05           0.02086939        -0.0102281941          0.011847935
## Lag 4e+05          -0.01260758        -0.0037541334         -0.008167550
## Lag 5e+05          -0.01998848         0.0005672827         -0.004071502
## Chain 4 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000          1.000000e+00           1.000000000
## Lag 1e+05  0.0224645616          1.784195e-02           0.010455881
## Lag 2e+05  0.0089836522          4.040036e-02          -0.008024200
## Lag 3e+05 -0.0141784233         -8.261718e-05          -0.003862752
## Lag 4e+05 -0.0091492865          2.992907e-02           0.003487751
## Lag 5e+05  0.0002028272         -3.524313e-03          -0.001226870
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.016053619          0.016979932          0.004451795
## Lag 2e+05         -0.028064110         -0.014351816          0.014273858
## Lag 3e+05          0.002085090         -0.003588906         -0.004309958
## Lag 4e+05         -0.001016541         -0.010506408          0.004567370
## Lag 5e+05         -0.028698303         -0.040388134          0.026708334
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.007854902           0.011695396          -0.012191540
## Lag 2e+05 -0.005259711          -0.013199314          -0.000860708
## Lag 3e+05  0.023212640           0.020899059           0.001762955
## Lag 4e+05  0.034799520           0.039169651           0.003499383
## Lag 5e+05  0.008316642          -0.005131585           0.041519582
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.019129467          0.013971358          0.014335328
## Lag 2e+05         -0.002905375          0.009345281         -0.011161619
## Lag 3e+05          0.013839668          0.017660618         -0.005213653
## Lag 4e+05          0.022730103          0.018097662          0.010710719
## Lag 5e+05         -0.015218436          0.002986982          0.008519737
## Chain 6 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.0081564273          0.0087633288          0.0070437959
## Lag 2e+05  0.0007834107         -0.0067433259          0.0149598041
## Lag 3e+05 -0.0134839361          0.0081827626          0.0006985165
## Lag 4e+05 -0.0067049282         -0.0009645366         -0.0017836691
## Lag 5e+05 -0.0152227996         -0.0036686520         -0.0209157048
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.007473357         -0.013586701          0.002125045
## Lag 2e+05         -0.003967014         -0.010837192          0.011437851
## Lag 3e+05         -0.004074421          0.001415328         -0.007356995
## Lag 4e+05          0.026260508         -0.005507372         -0.003251000
## Lag 5e+05         -0.028163935          0.008707329         -0.021063262
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.033540290         -0.0153248465          -0.009173664
## Lag 2e+05 -0.016990173         -0.0008163169           0.006012257
## Lag 3e+05  0.001463528         -0.0202440828           0.017539643
## Lag 4e+05  0.008396229         -0.0160918846          -0.016798487
## Lag 5e+05  0.003859869         -0.0208920113          -0.024695040
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.01063046          0.029248963         -0.009934672
## Lag 2e+05          -0.01162834          0.008072418         -0.018618860
## Lag 3e+05           0.01196546          0.037372123          0.002967483
## Lag 4e+05           0.01578000         -0.030478822          0.001248421
## Lag 5e+05           0.01399681         -0.012613542          0.001928380
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.012470353           0.003658270         -0.0277476196
## Lag 2e+05 -0.008046442           0.009763684         -0.0008693885
## Lag 3e+05 -0.022457371          -0.005274708         -0.0299199312
## Lag 4e+05 -0.018725001           0.008761441          0.0114885925
## Lag 5e+05  0.005299452          -0.009391127          0.0197180266
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05          0.012070816          -0.01414538          0.024201125
## Lag 2e+05         -0.018249011           0.01110926         -0.023028787
## Lag 3e+05          0.017811469          -0.02073407         -0.010077203
## Lag 4e+05          0.014853740           0.01365676          0.011524809
## Lag 5e+05         -0.009943323          -0.01517775          0.003422765
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.4498                0.2695                1.0898 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1977               -0.2121               -1.1982 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6528754             0.7875718             0.2758045 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8433102             0.8320154             0.2308482 
## Joint P-value (lower = worse):  0.7042222 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.84024              -1.12084              -0.60216 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.07758               0.02077              -0.37963 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4007743             0.2623556             0.5470695 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.9381661             0.9834285             0.7042179 
## Joint P-value (lower = worse):  0.8389077 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4961               -0.6390                0.2576 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1190               -0.5697                0.5317 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6198235             0.5228062             0.7967199 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.9052781             0.5689107             0.5949412 
## Joint P-value (lower = worse):  0.9484161 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.6077                1.5640                0.4276 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.5840                1.7052                1.5777 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.10789969            0.11782802            0.66894872 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.55925236            0.08815563            0.11462551 
## Joint P-value (lower = worse):  0.1424581 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.7565               -0.1949                1.8757 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.0085               -0.4302                1.1165 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.07900789            0.84547797            0.06069433 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.31320179            0.66703828            0.26418861 
## Joint P-value (lower = worse):  0.1941955 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.2579                0.7303               -1.0786 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.2551               -0.7524               -0.1256 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7965156             0.4651883             0.2807608 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.7986409             0.4518302             0.9000303 
## Joint P-value (lower = worse):  0.9569326 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4566               -0.4246               -0.7287 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.1565               -0.5067                1.4818 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6479364             0.6711539             0.4662087 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8756597             0.6123817             0.1384026 
## Joint P-value (lower = worse):  0.5309442 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.55076              -0.05389              -0.29861 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.18073              -1.14701               0.59100 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.5817984             0.9570241             0.7652376 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8565803             0.2513762             0.5545174 
## Joint P-value (lower = worse):  0.8805751 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                             Mean     SD Naive SE Time-series SE
## edges                  0.0003667 40.088  0.23145        0.23263
## nodefactor.deg.main.1 -0.0795333 45.078  0.26026        0.25871
## nodefactor.race..wa.B  0.6763000 16.041  0.09262        0.09394
## nodefactor.race..wa.H -0.4632000 23.583  0.13615        0.13844
## nodematch.race..wa.B   0.0135177  2.880  0.01663        0.01671
## nodematch.race..wa.H  -0.1525637  6.927  0.03999        0.03975
## nodematch.race..wa.O  -0.4243794 32.677  0.18866        0.18700
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%    75% 97.5%
## edges                 -78.50 -26.500 -0.5000 26.500 79.50
## nodefactor.deg.main.1 -89.00 -30.000  0.0000 30.000 89.00
## nodefactor.race..wa.B -30.52 -10.517  0.4832 11.483 32.48
## nodefactor.race..wa.H -46.34 -16.340 -0.3400 15.660 46.66
## nodematch.race..wa.B   -5.48  -2.480 -0.4798  1.520  6.52
## nodematch.race..wa.H  -13.18  -5.181 -0.1815  4.819 13.82
## nodematch.race..wa.O  -64.08 -23.081 -1.0808 21.919 63.92
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75567574
## nodefactor.deg.main.1 0.75567574            1.00000000
## nodefactor.race..wa.B 0.34878341            0.23595585
## nodefactor.race..wa.H 0.46408333            0.39925610
## nodematch.race..wa.B  0.05407078            0.02733345
## nodematch.race..wa.H  0.12069718            0.11931936
## nodematch.race..wa.O  0.78474192            0.57816856
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                           0.348783411           0.464083328
## nodefactor.deg.main.1           0.235955847           0.399256102
## nodefactor.race..wa.B           1.000000000           0.106971114
## nodefactor.race..wa.H           0.106971114           1.000000000
## nodematch.race..wa.B            0.300638176          -0.001563697
## nodematch.race..wa.H           -0.008770457           0.498985173
## nodematch.race..wa.O           -0.018362660          -0.037391781
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                          0.054070777          0.120697180
## nodefactor.deg.main.1          0.027333453          0.119319361
## nodefactor.race..wa.B          0.300638176         -0.008770457
## nodefactor.race..wa.H         -0.001563697          0.498985173
## nodematch.race..wa.B           1.000000000          0.007093001
## nodematch.race..wa.H           0.007093001          1.000000000
## nodematch.race..wa.O           0.004282978         -0.001179403
##                       nodematch.race..wa.O
## edges                          0.784741922
## nodefactor.deg.main.1          0.578168564
## nodefactor.race..wa.B         -0.018362660
## nodefactor.race..wa.H         -0.037391781
## nodematch.race..wa.B           0.004282978
## nodematch.race..wa.H          -0.001179403
## nodematch.race..wa.O           1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.020346379           0.007257429          0.0023991539
## Lag 2e+05  0.008336525           0.008624913          0.0264586129
## Lag 3e+05 -0.008185406          -0.034032252         -0.0005977681
## Lag 4e+05 -0.016419040          -0.032652433          0.0276314928
## Lag 5e+05 -0.007842153          -0.002968748          0.0045604650
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.005322937          0.003121216         -0.021671753
## Lag 2e+05           0.007151695          0.014134361         -0.010609441
## Lag 3e+05          -0.024763491         -0.007253842         -0.003569679
## Lag 4e+05          -0.007066556         -0.011904789          0.017600048
## Lag 5e+05           0.039670993          0.021367959          0.031751262
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0092642715
## Lag 2e+05        -0.0008065677
## Lag 3e+05         0.0016950608
## Lag 4e+05        -0.0248122777
## Lag 5e+05        -0.0256992975
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.033432452          -0.021140204          -0.001643601
## Lag 2e+05 -0.009539155          -0.002464447           0.011168133
## Lag 3e+05  0.039421042           0.025503390          -0.016759099
## Lag 4e+05 -0.005227752          -0.003702030           0.004159357
## Lag 5e+05 -0.013595720           0.015074988          -0.002094999
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.006490441         -0.010859749          0.014018834
## Lag 2e+05           0.005612046         -0.010218864         -0.041821296
## Lag 3e+05           0.014641463         -0.038114557          0.002095162
## Lag 4e+05          -0.017766035          0.027919064         -0.005688775
## Lag 5e+05          -0.019271565         -0.005792261         -0.017025754
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05        -0.0118583201
## Lag 2e+05        -0.0321352592
## Lag 3e+05         0.0181139106
## Lag 4e+05        -0.0269303725
## Lag 5e+05        -0.0001396649
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.008358189          -0.019641560           0.002205620
## Lag 2e+05 -0.009429479          -0.015899415          -0.019319928
## Lag 3e+05  0.020139587           0.015697208           0.005260236
## Lag 4e+05 -0.003190494           0.003094338           0.011448050
## Lag 5e+05  0.003302542           0.030350441           0.003683434
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.022040551          0.016087606         -0.009162634
## Lag 2e+05          -0.017510889         -0.013538269          0.002901417
## Lag 3e+05          -0.004109120          0.041005952         -0.005402268
## Lag 4e+05          -0.023275506          0.007468984         -0.005360512
## Lag 5e+05          -0.004071408          0.002865810         -0.018305342
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.005905203
## Lag 2e+05          0.006827892
## Lag 3e+05          0.026442015
## Lag 4e+05          0.006701167
## Lag 5e+05         -0.009486472
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.009659987            0.01079896          -0.005904286
## Lag 2e+05  0.004266297           -0.01140462           0.043051041
## Lag 3e+05  0.013721497            0.01611263          -0.007483191
## Lag 4e+05 -0.001237171            0.01227068           0.032858755
## Lag 5e+05 -0.001599683            0.01825667           0.020195799
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0065691335          0.011446100          0.010205913
## Lag 2e+05          0.0032697396         -0.025824317         -0.034037605
## Lag 3e+05         -0.0007877063          0.005548393          0.007604281
## Lag 4e+05         -0.0083929417          0.019109810          0.006101996
## Lag 5e+05         -0.0042518365          0.027201148         -0.009351544
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.004368116
## Lag 2e+05          0.015074423
## Lag 3e+05          0.011569052
## Lag 4e+05         -0.004801247
## Lag 5e+05          0.008002505
## Chain 5 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0128824505           0.004968885           0.020246188
## Lag 2e+05 -0.0008600489           0.006758939          -0.003393436
## Lag 3e+05 -0.0038571760           0.005820086           0.011088105
## Lag 4e+05 -0.0096543200          -0.004416776          -0.001946214
## Lag 5e+05  0.0037368822          -0.003221812           0.011761987
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05          -0.004322357           0.01505552         -0.023040228
## Lag 2e+05          -0.007859073          -0.01688641          0.005403930
## Lag 3e+05          -0.008792907          -0.01648602         -0.010315436
## Lag 4e+05           0.003951401          -0.01513773         -0.005091850
## Lag 5e+05           0.011894261          -0.02705870          0.003799587
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.024408207
## Lag 2e+05         -0.005702051
## Lag 3e+05         -0.015572724
## Lag 4e+05         -0.013748425
## Lag 5e+05          0.002397250
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.023450038          -0.020089147          0.0116543781
## Lag 2e+05 -0.001952285          -0.011631077          0.0058145025
## Lag 3e+05  0.037828921           0.011849570         -0.0008881523
## Lag 4e+05  0.000463904           0.007704660          0.0088855258
## Lag 5e+05  0.015062892          -0.004875415          0.0109068840
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.004107895         0.0002986658          0.004131773
## Lag 2e+05           0.007878321         0.0520089165          0.006513362
## Lag 3e+05          -0.012233228         0.0083847460         -0.041820513
## Lag 4e+05           0.011467523        -0.0036065884         -0.005123006
## Lag 5e+05           0.023830435         0.0020294657          0.030109413
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.020415890
## Lag 2e+05          0.002197100
## Lag 3e+05          0.024719054
## Lag 4e+05         -0.001904416
## Lag 5e+05          0.008128940
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.000000e+00           1.000000000
## Lag 1e+05 -0.002390172          6.969027e-03          -0.013287328
## Lag 2e+05 -0.016189638         -1.287775e-05           0.012327880
## Lag 3e+05  0.006999612          1.264744e-03          -0.013569386
## Lag 4e+05  0.001622088         -1.406119e-02          -0.030649081
## Lag 5e+05 -0.010399368         -1.313095e-02           0.006087377
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000           1.00000000
## Lag 1e+05           0.014046068          0.009281620           0.02900322
## Lag 2e+05          -0.007225317          0.029520009          -0.01475909
## Lag 3e+05          -0.031780039          0.024476791          -0.02320434
## Lag 4e+05           0.013664691         -0.002804322          -0.01425053
## Lag 5e+05          -0.008628453         -0.018140815          -0.02639694
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.003875352
## Lag 2e+05         -0.034757764
## Lag 3e+05          0.004239766
## Lag 4e+05          0.017164535
## Lag 5e+05         -0.016174285
## Chain 8 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000            1.00000000
## Lag 1e+05  0.03194833           0.008662342            0.05169358
## Lag 2e+05  0.01642757           0.005724186           -0.02332861
## Lag 3e+05 -0.01188635          -0.018220038            0.01870458
## Lag 4e+05  0.01574487           0.033037564           -0.01017263
## Lag 5e+05  0.01596508           0.006968348            0.03322637
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05          -0.002491915         -0.005158260         0.0157177324
## Lag 2e+05           0.004433865         -0.020937794         0.0005948879
## Lag 3e+05           0.009709178          0.013374941        -0.0004356022
## Lag 4e+05          -0.002578136          0.009232686         0.0159932375
## Lag 5e+05           0.021716248          0.019420266         0.0064974817
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0006988953
## Lag 2e+05         0.0266633979
## Lag 3e+05        -0.0192785315
## Lag 4e+05         0.0181656266
## Lag 5e+05        -0.0147040867
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.32937               1.17666               0.91220 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.04825               0.56778               0.90087 
##  nodematch.race..wa.O 
##               0.17095 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7418739             0.2393302             0.3616659 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9615144             0.5701828             0.3676576 
##  nodematch.race..wa.O 
##             0.8642615 
## Joint P-value (lower = worse):  0.860803 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.61479               0.53606               0.33620 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##              -1.12447              -0.33337              -0.03493 
##  nodematch.race..wa.O 
##               1.37407 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5386930             0.5919155             0.7367237 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2608124             0.7388525             0.9721342 
##  nodematch.race..wa.O 
##             0.1694192 
## Joint P-value (lower = worse):  0.7933805 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3535                1.3636               -1.9832 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.2037               -2.1218                0.4305 
##  nodematch.race..wa.O 
##                1.3037 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.72372673            0.17268046            0.04734771 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.22870741            0.03385666            0.66685409 
##  nodematch.race..wa.O 
##            0.19231989 
## Joint P-value (lower = worse):  0.03873953 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3656               -0.8904                0.6018 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.2599                1.4363                0.6860 
##  nodematch.race..wa.O 
##               -0.5614 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7146980             0.3732331             0.5473029 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.7949034             0.1509087             0.4927078 
##  nodematch.race..wa.O 
##             0.5745022 
## Joint P-value (lower = worse):  0.5760181 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.2076               -1.2937                0.2086 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.3264               -0.4162                0.6691 
##  nodematch.race..wa.O 
##               -0.8358 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8355108             0.1957532             0.8347363 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.1847174             0.6772757             0.5034156 
##  nodematch.race..wa.O 
##             0.4032946 
## Joint P-value (lower = worse):  0.5798393 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.9140                0.3557                0.8174 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.1499               -0.3872                1.0626 
##  nodematch.race..wa.O 
##                0.0714 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3606940             0.7220846             0.4137030 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2501857             0.6985740             0.2879734 
##  nodematch.race..wa.O 
##             0.9430815 
## Joint P-value (lower = worse):  0.8740967 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.4195                0.1408               -0.5261 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.3835                0.4382                1.8054 
##  nodematch.race..wa.O 
##               -0.3776 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.67484698            0.88802261            0.59880903 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.70132077            0.66125704            0.07101767 
##  nodematch.race..wa.O 
##            0.70573529 
## Joint P-value (lower = worse):  0.6427369 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.9586               -0.1157               -1.4526 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.2728                2.0765                0.1456 
##  nodematch.race..wa.O 
##               -0.1517 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.33774196            0.90791266            0.14632866 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.78503908            0.03784736            0.88423640 
##  nodematch.race..wa.O 
##            0.87943263 
## Joint P-value (lower = worse):  0.1592844 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 5

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  0.47593 40.071  0.23135        0.23067
## nodefactor.deg.main.1 -0.78657 45.209  0.26102        0.26141
## nodefactor.race..wa.B -0.32920 15.925  0.09194        0.09195
## nodefactor.race..wa.H -1.50490 23.465  0.13548        0.13432
## nodefactor.region.EW  -0.40953 18.781  0.10843        0.10754
## nodefactor.region.OW   0.88057 36.695  0.21186        0.21317
## nodematch.race..wa.B  -0.04772  2.880  0.01663        0.01659
## nodematch.race..wa.H  -0.51176  6.864  0.03963        0.03921
## nodematch.race..wa.O   1.68085 32.657  0.18854        0.18792
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%    75% 97.5%
## edges                 -77.50 -26.500  0.5000 27.500 78.50
## nodefactor.deg.main.1 -89.00 -31.000 -1.0000 29.000 89.00
## nodefactor.race..wa.B -31.52 -11.517 -0.5168 10.483 30.48
## nodefactor.race..wa.H -47.34 -17.340 -1.3400 13.660 45.66
## nodefactor.region.EW  -36.59 -13.588 -0.5885 12.412 36.41
## nodefactor.region.OW  -71.25 -24.255  0.7450 25.745 73.75
## nodematch.race..wa.B   -5.48  -2.480 -0.4798  1.520  5.52
## nodematch.race..wa.H  -13.18  -5.181 -1.1815  3.819 13.82
## nodematch.race..wa.O  -62.08 -20.081  1.9192 23.919 65.92
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75666469
## nodefactor.deg.main.1 0.75666469            1.00000000
## nodefactor.race..wa.B 0.34538833            0.23435368
## nodefactor.race..wa.H 0.46236378            0.39612780
## nodefactor.region.EW  0.38877954            0.30171174
## nodefactor.region.OW  0.66076311            0.45482557
## nodematch.race..wa.B  0.05426938            0.03365535
## nodematch.race..wa.H  0.11580470            0.11210820
## nodematch.race..wa.O  0.78819998            0.58094356
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.34538833            0.46236378
## nodefactor.deg.main.1            0.23435368            0.39612780
## nodefactor.race..wa.B            1.00000000            0.09885660
## nodefactor.race..wa.H            0.09885660            1.00000000
## nodefactor.region.EW             0.08981442            0.28433482
## nodefactor.region.OW             0.20818208            0.29471141
## nodematch.race..wa.B             0.30898892           -0.01268684
## nodematch.race..wa.H            -0.02744403            0.49717804
## nodematch.race..wa.O            -0.01560240           -0.03391010
##                       nodefactor.region.EW nodefactor.region.OW
## edges                          0.388779539           0.66076311
## nodefactor.deg.main.1          0.301711740           0.45482557
## nodefactor.race..wa.B          0.089814422           0.20818208
## nodefactor.race..wa.H          0.284334822           0.29471141
## nodefactor.region.EW           1.000000000           0.11313942
## nodefactor.region.OW           0.113139422           1.00000000
## nodematch.race..wa.B           0.001161155           0.03191665
## nodematch.race..wa.H           0.104108353           0.07681344
## nodematch.race..wa.O           0.265857231           0.53468551
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                          0.054269383         0.1158046992
## nodefactor.deg.main.1          0.033655353         0.1121082022
## nodefactor.race..wa.B          0.308988918        -0.0274440287
## nodefactor.race..wa.H         -0.012686836         0.4971780368
## nodefactor.region.EW           0.001161155         0.1041083533
## nodefactor.region.OW           0.031916654         0.0768134422
## nodematch.race..wa.B           1.000000000        -0.0096487696
## nodematch.race..wa.H          -0.009648770         1.0000000000
## nodematch.race..wa.O           0.004910681         0.0006486809
##                       nodematch.race..wa.O
## edges                         0.7881999818
## nodefactor.deg.main.1         0.5809435560
## nodefactor.race..wa.B        -0.0156024023
## nodefactor.race..wa.H        -0.0339101030
## nodefactor.region.EW          0.2658572311
## nodefactor.region.OW          0.5346855062
## nodematch.race..wa.B          0.0049106809
## nodematch.race..wa.H          0.0006486809
## nodematch.race..wa.O          1.0000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.015306435          -0.018052938            0.00132814
## Lag 2e+05 -0.002918860           0.025922884           -0.01506075
## Lag 3e+05  0.004425414          -0.005903431            0.01340845
## Lag 4e+05  0.029138614           0.010364324            0.01215767
## Lag 5e+05 -0.025651556          -0.009713273           -0.02455062
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.022348759         -0.012341794          0.005262533
## Lag 2e+05          -0.023673776         -0.030555899          0.005043605
## Lag 3e+05           0.006939928         -0.004122538          0.005170901
## Lag 4e+05           0.004714846         -0.009094313          0.012680795
## Lag 5e+05           0.005556443         -0.019156415         -0.013788248
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.017734606          0.005118715          0.020610373
## Lag 2e+05          0.006912085          0.002286716         -0.006841280
## Lag 3e+05          0.022474364         -0.031339122          0.001099243
## Lag 4e+05         -0.011148104          0.008197210          0.032459904
## Lag 5e+05         -0.012516422          0.007180503         -0.010381251
## Chain 2 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0131101818           0.018947703           0.020371391
## Lag 2e+05 -0.0017693739          -0.008455363           0.002862859
## Lag 3e+05  0.0021289481          -0.006500097          -0.018891447
## Lag 4e+05  0.0007318604          -0.012799490           0.004167525
## Lag 5e+05 -0.0127590483          -0.036307119          -0.009775620
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.008126948          0.015852547          0.002965173
## Lag 2e+05           0.003764299          0.014686695         -0.012398148
## Lag 3e+05          -0.031758061         -0.008976189         -0.030125373
## Lag 4e+05          -0.003743919          0.005114454          0.008317561
## Lag 5e+05          -0.005915130          0.005938460         -0.010309619
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000         1.0000000000
## Lag 1e+05           0.01476034         -0.023544847        -0.0127970573
## Lag 2e+05          -0.01344060         -0.020653545         0.0149560035
## Lag 3e+05           0.01122950         -0.008804326        -0.0005471229
## Lag 4e+05           0.02721754         -0.017387914        -0.0019084030
## Lag 5e+05          -0.03439105          0.004839526        -0.0024251507
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.010447328         -0.0022922647           0.016690741
## Lag 2e+05  0.002562907          0.0167040053           0.007914421
## Lag 3e+05  0.009542835          0.0108454200           0.010639006
## Lag 4e+05 -0.020837504         -0.0088594085           0.003679102
## Lag 5e+05  0.008045893         -0.0001280241           0.005148860
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.002825576         0.0182685898         -0.006363499
## Lag 2e+05           0.005749679         0.0050833991          0.013130682
## Lag 3e+05          -0.003501010        -0.0101991373         -0.013225344
## Lag 4e+05           0.015505487         0.0002853342         -0.016784753
## Lag 5e+05           0.026992074        -0.0044628958         -0.003044371
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.000000e+00          1.000000000
## Lag 1e+05         -0.001282273         4.758006e-03          0.005160048
## Lag 2e+05          0.005670709        -1.754309e-02          0.027880685
## Lag 3e+05         -0.030596267        -2.439940e-03          0.012349108
## Lag 4e+05         -0.010581956         2.735818e-02         -0.012393788
## Lag 5e+05          0.004535701        -4.229668e-05         -0.001762647
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.025260542          -0.026998767         -0.0203873053
## Lag 2e+05 -0.005809487           0.029807133         -0.0002485406
## Lag 3e+05 -0.026344268           0.008551758          0.0277406828
## Lag 4e+05  0.004155856           0.015435780          0.0120652261
## Lag 5e+05 -0.005781379          -0.036251018          0.0015232837
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0159506737          0.004718468         -0.008474810
## Lag 2e+05          0.0037765356          0.008062613         -0.006520565
## Lag 3e+05         -0.0019222818          0.003361787         -0.008091097
## Lag 4e+05         -0.0007515139          0.007874367         -0.005389109
## Lag 5e+05          0.0075045902         -0.006946675         -0.007486147
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000         1.0000000000          1.000000000
## Lag 1e+05         0.0263192993        -0.0309300565         -0.027798249
## Lag 2e+05         0.0125463265         0.0003658643         -0.013285681
## Lag 3e+05        -0.0247372411         0.0212922262         -0.010045939
## Lag 4e+05         0.0002955329         0.0078951133          0.006639135
## Lag 5e+05         0.0046948821        -0.0087321970         -0.006410832
## Chain 5 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.0007767478          -0.033445718         -0.0112920446
## Lag 2e+05  0.0126253264           0.012730531         -0.0076570745
## Lag 3e+05 -0.0024801973           0.008120142          0.0179439855
## Lag 4e+05  0.0078466505           0.011846235          0.0001473282
## Lag 5e+05  0.0006099981           0.006420157          0.0125179207
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.014625641         -0.014184193          0.010341751
## Lag 2e+05           0.011924162          0.011923194         -0.015350243
## Lag 3e+05          -0.006849913         -0.036408412         -0.007563223
## Lag 4e+05          -0.004274138          0.011827097          0.010560889
## Lag 5e+05           0.006557485         -0.005417356          0.032991211
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000         1.0000000000          1.000000000
## Lag 1e+05        -0.0048137947        -0.0004479711         -0.005999612
## Lag 2e+05         0.0133257104        -0.0209609187          0.008132494
## Lag 3e+05        -0.0099064445        -0.0476163534         -0.010294433
## Lag 4e+05        -0.0090396159        -0.0014205766         -0.001789669
## Lag 5e+05         0.0006072016        -0.0277650004          0.008619264
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.009391393           0.011945380           0.002310709
## Lag 2e+05  0.001398434          -0.010611825           0.017953613
## Lag 3e+05 -0.007346506           0.003391769          -0.008381760
## Lag 4e+05 -0.012126942          -0.021727262           0.008481824
## Lag 5e+05 -0.009122910          -0.001547922          -0.004626226
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000         1.0000000000          1.000000000
## Lag 1e+05         -0.0139031380         0.0049132295         -0.020281250
## Lag 2e+05          0.0007152583         0.0105711430         -0.001647741
## Lag 3e+05          0.0062528211         0.0180638879         -0.004061461
## Lag 4e+05         -0.0155042732        -0.0067776650          0.002843936
## Lag 5e+05         -0.0110546559        -0.0004468045          0.007485176
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000         1.0000000000
## Lag 1e+05         -0.023598350        -0.0104074430         0.0127384143
## Lag 2e+05         -0.005480389         0.0031745426         0.0009307375
## Lag 3e+05         -0.004101450         0.0039285611         0.0001273943
## Lag 4e+05         -0.005725877         0.0001523318        -0.0031507134
## Lag 5e+05         -0.037219023        -0.0009030414        -0.0098082537
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.011594643          0.0002826664          -0.013071480
## Lag 2e+05  0.012237969         -0.0178709801          -0.002065485
## Lag 3e+05  0.003610117          0.0005950716           0.018719633
## Lag 4e+05  0.013104222          0.0030829340          -0.006131741
## Lag 5e+05 -0.017952335         -0.0220786928           0.001486529
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.025017860         0.0044155118         -0.004666238
## Lag 2e+05           0.005843071         0.0008068439          0.022664306
## Lag 3e+05          -0.020877121        -0.0143439536         -0.004133951
## Lag 4e+05           0.021106151         0.0190497400          0.001038634
## Lag 5e+05          -0.019865626        -0.0028894109         -0.011822922
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.003827034         -0.007935242          0.008313818
## Lag 2e+05         -0.020531069         -0.006467763          0.010320889
## Lag 3e+05          0.003889147          0.006805101          0.001265917
## Lag 4e+05         -0.001976969         -0.006885797         -0.001340558
## Lag 5e+05         -0.007986954         -0.006658264         -0.010960895
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.010399245          -0.006682664           0.009256780
## Lag 2e+05  0.002897909           0.018185848           0.026399108
## Lag 3e+05 -0.020300118           0.004607525           0.002754848
## Lag 4e+05 -0.014557238          -0.007906497           0.018838609
## Lag 5e+05  0.008274621          -0.002117132           0.023194382
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000         1.0000000000
## Lag 1e+05           0.002216389         0.0004650981         0.0101808509
## Lag 2e+05           0.010330800        -0.0360264596         0.0374094109
## Lag 3e+05          -0.028218980         0.0466678545         0.0017403295
## Lag 4e+05           0.013598505         0.0101534953        -0.0007818516
## Lag 5e+05          -0.012772702        -0.0032311997         0.0074307124
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.018430728          0.027086471         -0.003957709
## Lag 2e+05          0.015118497          0.009490426         -0.010949490
## Lag 3e+05          0.011214195          0.013294020         -0.022170848
## Lag 4e+05         -0.006568312         -0.011633989         -0.030351381
## Lag 5e+05         -0.021679373          0.007529639          0.005215417
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.6959                0.1829                0.4877 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.7269                0.1075               -2.5487 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.6848               -1.0162               -2.1654 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.08989815            0.85484227            0.62577610 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.46726614            0.91442325            0.01081184 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.49347777            0.30955724            0.03035550 
## Joint P-value (lower = worse):  0.07582541 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             -0.007836             -0.380024             -0.238627 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             -0.108207              1.155100              0.057517 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             -0.074759              1.167007              0.517084 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9937481             0.7039280             0.8113950 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9138312             0.2480494             0.9541333 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.9404061             0.2432076             0.6050976 
## Joint P-value (lower = worse):  0.8434678 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.3436               -0.8177               -0.8565 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.3646                0.4779                0.5406 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.7898                1.0666                0.7338 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7311181             0.4135347             0.3917005 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.7153757             0.6327363             0.5887490 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.4296624             0.2861633             0.4630463 
## Joint P-value (lower = worse):  0.730802 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -1.71705              -1.00242               1.11639 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.90682              -0.04609              -0.67051 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               1.36265              -1.43651              -1.60100 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.08597032            0.31614142            0.26425710 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.36450386            0.96324135            0.50252986 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.17299378            0.15085836            0.10937725 
## Joint P-value (lower = worse):  0.3650856 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.44027               1.55756              -0.20203 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -1.29757              -2.12386              -0.40313 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               0.08772              -0.15291               0.55270 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.65973912            0.11933876            0.83989078 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.19443475            0.03368217            0.68685593 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.93009707            0.87846572            0.58047174 
## Joint P-value (lower = worse):  0.1470618 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3070               -0.5344                1.1282 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.8037               -1.7452                1.0294 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.3221                0.6083               -0.4681 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.75880940            0.59307742            0.25923623 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.42159043            0.08095277            0.30328192 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.18613691            0.54296043            0.63969356 
## Joint P-value (lower = worse):  0.184885 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3606                1.1080               -0.5898 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.0073               -0.9405                0.7627 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.5817               -2.7745                0.4128 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##           0.718405510           0.267876090           0.555349389 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##           0.313812353           0.346954918           0.445664910 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##           0.560740977           0.005528252           0.679788317 
## Joint P-value (lower = worse):  0.02274662 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.8159                0.2994               -0.4321 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.9135               -0.4469               -0.5666 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.2939               -0.3949               -1.1324 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.4145300             0.7646370             0.6656552 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.3610058             0.6549203             0.5709743 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.1957132             0.6929414             0.2574465 
## Joint P-value (lower = worse):  0.5995688 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 6

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                            Mean     SD Naive SE Time-series SE
## edges                  3.036133 40.139  0.23174        0.22932
## nodefactor.deg.main.1  3.000567 45.354  0.26185        0.26009
## nodefactor.race..wa.B -0.087833 15.960  0.09214        0.09279
## nodefactor.race..wa.H  1.645867 23.638  0.13647        0.13636
## nodefactor.region.EW  -0.012600 18.897  0.10910        0.10870
## nodefactor.region.OW   2.469367 36.366  0.20996        0.20826
## nodematch.race..wa.B   0.005751  2.890  0.01669        0.01668
## nodematch.race..wa.H   0.061336  6.949  0.04012        0.04060
## nodematch.race..wa.O   1.576854 32.785  0.18929        0.18827
## absdiff.sqrt.age       5.325223 45.279  0.26142        0.25908
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%    75% 97.5%
## edges                 -75.50 -23.500  3.5000 29.500 81.50
## nodefactor.deg.main.1 -86.00 -28.000  3.0000 34.000 92.00
## nodefactor.race..wa.B -30.52 -10.517 -0.5168 10.483 31.48
## nodefactor.race..wa.H -44.34 -14.340  1.6600 17.660 47.66
## nodefactor.region.EW  -36.59 -12.588 -0.5885 12.412 37.41
## nodefactor.region.OW  -68.25 -22.255  2.7450 26.745 73.75
## nodematch.race..wa.B   -5.48  -2.480 -0.4798  1.520  5.52
## nodematch.race..wa.H  -13.18  -5.181 -0.1815  4.819 13.82
## nodematch.race..wa.O  -62.08 -21.081  0.9192 23.919 64.92
## absdiff.sqrt.age      -82.95 -25.513  4.9456 35.828 95.00
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75679144
## nodefactor.deg.main.1 0.75679144            1.00000000
## nodefactor.race..wa.B 0.34088449            0.23315514
## nodefactor.race..wa.H 0.46682144            0.39489733
## nodefactor.region.EW  0.39132611            0.29605980
## nodefactor.region.OW  0.65928284            0.45763357
## nodematch.race..wa.B  0.05914573            0.04279328
## nodematch.race..wa.H  0.12844616            0.12155482
## nodematch.race..wa.O  0.78498271            0.58210417
## absdiff.sqrt.age      0.73425035            0.55529280
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.34088449           0.466821441
## nodefactor.deg.main.1            0.23315514           0.394897328
## nodefactor.race..wa.B            1.00000000           0.107193923
## nodefactor.race..wa.H            0.10719392           1.000000000
## nodefactor.region.EW             0.07791901           0.298919920
## nodefactor.region.OW             0.20214304           0.287051440
## nodematch.race..wa.B             0.31316823          -0.005901527
## nodematch.race..wa.H            -0.01061830           0.502863426
## nodematch.race..wa.O            -0.02617551          -0.034735421
## absdiff.sqrt.age                 0.25162652           0.350603871
##                       nodefactor.region.EW nodefactor.region.OW
## edges                          0.391326114           0.65928284
## nodefactor.deg.main.1          0.296059799           0.45763357
## nodefactor.race..wa.B          0.077919007           0.20214304
## nodefactor.race..wa.H          0.298919920           0.28705144
## nodefactor.region.EW           1.000000000           0.10990237
## nodefactor.region.OW           0.109902374           1.00000000
## nodematch.race..wa.B           0.005117035           0.03089164
## nodematch.race..wa.H           0.122424431           0.07154405
## nodematch.race..wa.O           0.265983367           0.53599269
## absdiff.sqrt.age               0.282356620           0.48660288
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                         5.914573e-02         1.284462e-01
## nodefactor.deg.main.1         4.279328e-02         1.215548e-01
## nodefactor.race..wa.B         3.131682e-01        -1.061830e-02
## nodefactor.race..wa.H        -5.901527e-03         5.028634e-01
## nodefactor.region.EW          5.117035e-03         1.224244e-01
## nodefactor.region.OW          3.089164e-02         7.154405e-02
## nodematch.race..wa.B          1.000000e+00        -1.094911e-05
## nodematch.race..wa.H         -1.094911e-05         1.000000e+00
## nodematch.race..wa.O          4.653035e-03         5.624247e-03
## absdiff.sqrt.age              4.487385e-02         9.655965e-02
##                       nodematch.race..wa.O absdiff.sqrt.age
## edges                          0.784982707       0.73425035
## nodefactor.deg.main.1          0.582104171       0.55529280
## nodefactor.race..wa.B         -0.026175510       0.25162652
## nodefactor.race..wa.H         -0.034735421       0.35060387
## nodefactor.region.EW           0.265983367       0.28235662
## nodefactor.region.OW           0.535992694       0.48660288
## nodematch.race..wa.B           0.004653035       0.04487385
## nodematch.race..wa.H           0.005624247       0.09655965
## nodematch.race..wa.O           1.000000000       0.57135652
## absdiff.sqrt.age               0.571356515       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.003525380          -0.005947055         -0.0138529089
## Lag 2e+05  0.002650839          -0.006208131          0.0067446142
## Lag 3e+05  0.006482064          -0.012386904         -0.0236135291
## Lag 4e+05 -0.025657403          -0.027468777         -0.0001782027
## Lag 5e+05  0.003464240           0.008215969         -0.0132382485
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000         1.0000000000          1.000000000
## Lag 1e+05            0.01222040         0.0098701402          0.008803227
## Lag 2e+05           -0.02196102         0.0233150288          0.033912992
## Lag 3e+05           -0.01055248         0.0009157982         -0.002385281
## Lag 4e+05           -0.01108913         0.0029040056         -0.002851153
## Lag 5e+05           -0.02365266        -0.0230257971          0.019007198
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.011201420          0.038493233          0.008397704
## Lag 2e+05          0.001090390         -0.003734910          0.016578998
## Lag 3e+05         -0.002957922          0.001188365          0.027616627
## Lag 4e+05         -0.035883724         -0.013582472          0.005207448
## Lag 5e+05          0.023136778         -0.013789916         -0.006364059
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05    -0.0116829827
## Lag 2e+05    -0.0094194502
## Lag 3e+05    -0.0009110845
## Lag 4e+05    -0.0404870523
## Lag 5e+05    -0.0007622560
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05 -0.031919346          -0.015841994           -0.01149043
## Lag 2e+05 -0.003166948          -0.025931591           -0.01908299
## Lag 3e+05  0.031966234           0.032277641           -0.01106794
## Lag 4e+05  0.005558704          -0.010517414           -0.01146389
## Lag 5e+05 -0.018594835          -0.004398479            0.02780604
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.048422868         -0.020357805         -0.017600267
## Lag 2e+05           0.022819786         -0.003341520          0.010744515
## Lag 3e+05           0.002651218         -0.007991695          0.009588094
## Lag 4e+05          -0.012307167         -0.003331527         -0.028285092
## Lag 5e+05           0.013094326         -0.021119152         -0.015114698
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.003377120          0.009366792         -0.007388680
## Lag 2e+05          0.018403909         -0.013284202         -0.006054701
## Lag 3e+05         -0.005355753         -0.017973034          0.020217681
## Lag 4e+05          0.027839266         -0.005827875         -0.001293279
## Lag 5e+05         -0.005872895          0.035720949         -0.020722114
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.021033201
## Lag 2e+05     -0.012039030
## Lag 3e+05      0.008318685
## Lag 4e+05      0.021597542
## Lag 5e+05     -0.009519221
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.012136667           0.009915924          -0.011598561
## Lag 2e+05 -0.044755115          -0.031677419          -0.022369786
## Lag 3e+05  0.001841987           0.015403488          -0.001669568
## Lag 4e+05 -0.006624650          -0.017690808          -0.029496836
## Lag 5e+05 -0.018458553          -0.010738995          -0.008244597
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.000000e+00          1.000000000
## Lag 1e+05           0.006710914        -6.633948e-03         -0.036688612
## Lag 2e+05          -0.023287517        -7.356797e-03         -0.024298586
## Lag 3e+05          -0.004828836        -6.775267e-05         -0.024445962
## Lag 4e+05           0.012323691         1.332838e-02          0.006662862
## Lag 5e+05          -0.018789115         2.043752e-02         -0.026441648
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.01466441          0.032863503         -0.016471913
## Lag 2e+05          -0.01604466          0.009461590         -0.030383400
## Lag 3e+05          -0.02185478          0.014120354         -0.008890067
## Lag 4e+05          -0.01955723          0.013455177          0.006159624
## Lag 5e+05          -0.01108289         -0.004100987          0.004704283
##           absdiff.sqrt.age
## Lag 0           1.00000000
## Lag 1e+05      -0.03154152
## Lag 2e+05      -0.04540585
## Lag 3e+05       0.00102928
## Lag 4e+05      -0.02230002
## Lag 5e+05      -0.01111823
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.012912349          0.0084845483         -0.0103706780
## Lag 2e+05 -0.001994479         -0.0038838674          0.0156029241
## Lag 3e+05 -0.001324666          0.0049230681          0.0155268284
## Lag 4e+05 -0.022066024         -0.0267043042          0.0215430485
## Lag 5e+05  0.010666914         -0.0008148622          0.0004980592
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.007867186         -0.008773321         -0.012665969
## Lag 2e+05           0.004882148         -0.008836749         -0.015706595
## Lag 3e+05           0.008026628          0.007753655         -0.012247478
## Lag 4e+05          -0.020299065         -0.013087254          0.003170685
## Lag 5e+05          -0.013328686         -0.019227111          0.008586260
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.002901487          0.013958607          0.002103240
## Lag 2e+05         -0.019354702          0.015219702         -0.008607253
## Lag 3e+05         -0.003436107         -0.010506831          0.003252033
## Lag 4e+05          0.015225248         -0.016147658         -0.005759139
## Lag 5e+05         -0.008337202          0.002882331          0.018208769
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.001606929
## Lag 2e+05      0.004638391
## Lag 3e+05      0.014221249
## Lag 4e+05      0.004492374
## Lag 5e+05     -0.002668298
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.013357999          -0.024688436           0.013450679
## Lag 2e+05 -0.013439290          -0.003946531           0.002529023
## Lag 3e+05 -0.011429223          -0.008271233          -0.015790217
## Lag 4e+05  0.019035307           0.010506640           0.010941237
## Lag 5e+05  0.001421248           0.004295797          -0.008716897
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.022234893         -0.009647018         -0.027540949
## Lag 2e+05          -0.014895510         -0.025062401         -0.014680512
## Lag 3e+05          -0.007155976         -0.019444813         -0.006754539
## Lag 4e+05          -0.008482358          0.013127562         -0.015949691
## Lag 5e+05           0.026320673         -0.003592270         -0.009814384
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.013309315          0.011555677         -0.019676039
## Lag 2e+05          0.010165583         -0.015543039         -0.013009029
## Lag 3e+05         -0.007021470         -0.010458431          0.022492434
## Lag 4e+05         -0.037763202          0.001802079          0.025786467
## Lag 5e+05          0.009848782          0.026872257          0.004224709
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.005168424
## Lag 2e+05     -0.027125651
## Lag 3e+05     -0.008583283
## Lag 4e+05      0.033031827
## Lag 5e+05      0.022661333
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.008253534           0.015898759           0.032063248
## Lag 2e+05  0.002474595           0.021039335          -0.023615082
## Lag 3e+05  0.023619901           0.024602429          -0.004488667
## Lag 4e+05 -0.026360181          -0.006133302           0.006938368
## Lag 5e+05  0.019182125           0.012223013           0.005978271
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.000000e+00
## Lag 1e+05           0.005115454         -0.030780080        -8.062468e-03
## Lag 2e+05           0.000484256          0.006346438        -3.433132e-05
## Lag 3e+05           0.012767446          0.017141250         2.749321e-02
## Lag 4e+05          -0.006862961         -0.008052323        -2.778476e-02
## Lag 5e+05           0.012875013         -0.006732726         2.243729e-03
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.018704893          0.011914458         0.0039600651
## Lag 2e+05         -0.017131962          0.015882479         0.0115316266
## Lag 3e+05          0.001827136          0.006646805        -0.0006106429
## Lag 4e+05          0.019590327         -0.002387818        -0.0185101675
## Lag 5e+05          0.012888176          0.008613483         0.0248701675
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05     0.0007974126
## Lag 2e+05     0.0071893861
## Lag 3e+05    -0.0040030835
## Lag 4e+05    -0.0064511000
## Lag 5e+05     0.0031036223
## Chain 7 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0001802699           0.008685442           0.046315981
## Lag 2e+05 -0.0049541976          -0.008099083          -0.004700357
## Lag 3e+05 -0.0033122765           0.002586778           0.010180023
## Lag 4e+05  0.0032572790           0.001953264           0.004077353
## Lag 5e+05 -0.0100894642           0.001063313          -0.009567668
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000         1.0000000000
## Lag 1e+05           0.049855195         0.0149911755        -0.0005039103
## Lag 2e+05           0.016154316         0.0037906499        -0.0036587992
## Lag 3e+05           0.018223495        -0.0346875328         0.0143919830
## Lag 4e+05          -0.014941266         0.0001791233         0.0220529506
## Lag 5e+05           0.007956114        -0.0259601736        -0.0213489461
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.012889698          0.017631250         -0.011776658
## Lag 2e+05          0.019958925          0.015222414         -0.015334751
## Lag 3e+05         -0.007140378          0.003454250         -0.002745299
## Lag 4e+05          0.004748315         -0.003132181          0.005132262
## Lag 5e+05         -0.018065832          0.001888240         -0.012204515
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.021901412
## Lag 2e+05     -0.016096734
## Lag 3e+05     -0.002502099
## Lag 4e+05     -0.007300720
## Lag 5e+05      0.002563104
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.011742886          -0.011416213         -0.0004473439
## Lag 2e+05 -0.009586745           0.005073916          0.0044085935
## Lag 3e+05 -0.002078646          -0.003268665         -0.0053718574
## Lag 4e+05  0.012356225           0.019547148          0.0107660564
## Lag 5e+05  0.015798013           0.010098175          0.0279004412
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000         1.0000000000
## Lag 1e+05          0.0300636792         -0.002017380        -0.0008925593
## Lag 2e+05          0.0150791552         -0.009122804        -0.0288135579
## Lag 3e+05         -0.0003032291         -0.009722598        -0.0149616597
## Lag 4e+05          0.0192007382          0.016900698         0.0035640685
## Lag 5e+05         -0.0227410332         -0.004199823        -0.0109229387
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.005219392          0.023250794         -0.004937892
## Lag 2e+05          0.011332192          0.012948200         -0.026657161
## Lag 3e+05         -0.013193865          0.004780372          0.005862342
## Lag 4e+05         -0.014399964          0.028150625          0.009929328
## Lag 5e+05          0.014346074         -0.025629618          0.017369591
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.012501484
## Lag 2e+05      0.002732422
## Lag 3e+05     -0.005926844
## Lag 4e+05      0.036510532
## Lag 5e+05     -0.012708863
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.3275               -2.1777                0.0214 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.5890               -0.8254               -1.1016 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.8803                0.1926               -0.7592 
##      absdiff.sqrt.age 
##               -0.5819 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.18435506            0.02942797            0.98292434 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.11206200            0.40917087            0.27063277 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.37871390            0.84723488            0.44773132 
##      absdiff.sqrt.age 
##            0.56062253 
## Joint P-value (lower = worse):  0.2891199 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             -0.364236              0.436802             -0.251930 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              0.897960             -0.112579             -0.002702 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              0.245986              1.376119             -0.784950 
##      absdiff.sqrt.age 
##              0.028548 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7156819             0.6622548             0.8010949 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.3692067             0.9103640             0.9978444 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8056932             0.1687848             0.4324831 
##      absdiff.sqrt.age 
##             0.9772252 
## Joint P-value (lower = worse):  0.8827967 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.6407                0.9586               -1.6891 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.2732                0.8059               -0.1146 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.2465                0.4348               -0.2693 
##      absdiff.sqrt.age 
##                0.3946 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.52171888            0.33775463            0.09120271 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.78471004            0.42028388            0.90878777 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.21257724            0.66367593            0.78771062 
##      absdiff.sqrt.age 
##            0.69314732 
## Joint P-value (lower = worse):  0.1910543 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.7890                1.2221               -1.7042 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.1695                0.2208                0.8489 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.1499                1.4493                1.9785 
##      absdiff.sqrt.age 
##                0.1967 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.43012481            0.22165883            0.08834964 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.86537594            0.82528423            0.39594956 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.25017549            0.14725060            0.04787553 
##      absdiff.sqrt.age 
##            0.84408351 
## Joint P-value (lower = worse):  0.3636991 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                2.0294                1.0103               -1.3629 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                1.2965                0.1624                0.2034 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.1608                1.1216                1.5055 
##      absdiff.sqrt.age 
##                1.2263 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.04241846            0.31237014            0.17290991 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.19481520            0.87100672            0.83883526 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.24572014            0.26204655            0.13220562 
##      absdiff.sqrt.age 
##            0.22008473 
## Joint P-value (lower = worse):  0.2377963 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3757               -0.8571               -1.3871 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.4681               -0.9130               -0.2103 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.9302               -0.7613                0.9851 
##      absdiff.sqrt.age 
##                0.4068 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7071388             0.3913866             0.1654066 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.1420857             0.3612551             0.8334070 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3522914             0.4464877             0.3245971 
##      absdiff.sqrt.age 
##             0.6841911 
## Joint P-value (lower = worse):  0.5337654 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.1597                0.3011               -0.9576 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.5312               -0.1916               -0.2759 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.0039               -0.1191                1.0372 
##      absdiff.sqrt.age 
##                0.3885 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8731123             0.7633553             0.3382472 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5952904             0.8480210             0.7826535 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3154118             0.9051800             0.2996273 
##      absdiff.sqrt.age 
##             0.6976633 
## Joint P-value (lower = worse):  0.8806737 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.5718               -0.1604               -0.6994 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.2117                1.0104                0.1837 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1305                0.5924                0.8583 
##      absdiff.sqrt.age 
##                0.1324 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5674725             0.8725316             0.4842915 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8323300             0.3123027             0.8542439 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8961811             0.5535766             0.3907472 
##      absdiff.sqrt.age 
##             0.8946793 
## Joint P-value (lower = worse):  0.9678352 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 7

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  2.56020 58.391  0.33712        0.35249
## nodefactor.deg.main.1  1.74833 60.531  0.34948        0.35604
## nodefactor.race..wa.B -0.46277 19.538  0.11280        0.11922
## nodefactor.race..wa.H  0.34290 29.713  0.17155        0.18463
## nodefactor.region.EW   0.07413 23.503  0.13570        0.13976
## nodefactor.region.OW   2.03730 47.684  0.27530        0.28500
## concurrent             2.35600 52.251  0.30167        0.31542
## nodematch.race..wa.B  -0.12498  2.958  0.01708        0.01791
## nodematch.race..wa.H   0.22560  7.394  0.04269        0.04895
## nodematch.race..wa.O   2.47709 44.361  0.25612        0.26483
## absdiff.sqrt.age       3.87738 57.449  0.33168        0.33551
## 
## 2. Quantiles for each variable:
## 
##                          2.5%     25%     50%    75%  97.5%
## edges                 -111.50 -37.500  2.5000 42.500 116.50
## nodefactor.deg.main.1 -117.00 -39.000  2.0000 43.000 120.00
## nodefactor.race..wa.B  -38.52 -13.517 -0.5168 12.483  38.48
## nodefactor.race..wa.H  -57.34 -20.340  0.6600 20.660  58.66
## nodefactor.region.EW   -45.59 -15.588 -0.5885 16.412  46.41
## nodefactor.region.OW   -90.25 -30.255  1.7450 33.745  96.75
## concurrent            -100.00 -33.000  2.0000 38.000 105.00
## nodematch.race..wa.B    -5.48  -2.480 -0.4798  1.520   6.52
## nodematch.race..wa.H   -13.18  -5.181 -0.1815  4.819  15.82
## nodematch.race..wa.O   -83.08 -28.081  1.9192 31.919  89.92
## absdiff.sqrt.age      -106.95 -35.317  3.6019 42.581 117.23
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.81532396
## nodefactor.deg.main.1 0.81532396            1.00000000
## nodefactor.race..wa.B 0.40940550            0.31666461
## nodefactor.race..wa.H 0.53983925            0.48026138
## nodefactor.region.EW  0.46367902            0.37851973
## nodefactor.region.OW  0.73369985            0.56627424
## concurrent            0.95337514            0.77428823
## nodematch.race..wa.B  0.08308939            0.06171822
## nodematch.race..wa.H  0.16762233            0.16518989
## nodematch.race..wa.O  0.84248329            0.67197148
## absdiff.sqrt.age      0.84390304            0.68902688
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.40940550            0.53983925
## nodefactor.deg.main.1            0.31666461            0.48026138
## nodefactor.race..wa.B            1.00000000            0.18652647
## nodefactor.race..wa.H            0.18652647            1.00000000
## nodefactor.region.EW             0.14882036            0.35058572
## nodefactor.region.OW             0.27779864            0.37415021
## concurrent                       0.39628243            0.52824275
## nodematch.race..wa.B             0.36149729            0.01141444
## nodematch.race..wa.H             0.01880424            0.56208942
## nodematch.race..wa.O             0.08861357            0.11072056
## absdiff.sqrt.age                 0.34356660            0.45917886
##                       nodefactor.region.EW nodefactor.region.OW concurrent
## edges                           0.46367902           0.73369985 0.95337514
## nodefactor.deg.main.1           0.37851973           0.56627424 0.77428823
## nodefactor.race..wa.B           0.14882036           0.27779864 0.39628243
## nodefactor.race..wa.H           0.35058572           0.37415021 0.52824275
## nodefactor.region.EW            1.00000000           0.21260844 0.43892589
## nodefactor.region.OW            0.21260844           1.00000000 0.69444399
## concurrent                      0.43892589           0.69444399 1.00000000
## nodematch.race..wa.B            0.01808728           0.05140923 0.07919731
## nodematch.race..wa.H            0.13950353           0.11100574 0.16821251
## nodematch.race..wa.O            0.35349339           0.63647814 0.79437030
## absdiff.sqrt.age                0.39247342           0.62043941 0.80182799
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                           0.08308939          0.167622327
## nodefactor.deg.main.1           0.06171822          0.165189888
## nodefactor.race..wa.B           0.36149729          0.018804236
## nodefactor.race..wa.H           0.01141444          0.562089421
## nodefactor.region.EW            0.01808728          0.139503533
## nodefactor.region.OW            0.05140923          0.111005737
## concurrent                      0.07919731          0.168212515
## nodematch.race..wa.B            1.00000000         -0.012255626
## nodematch.race..wa.H           -0.01225563          1.000000000
## nodematch.race..wa.O            0.01475949          0.006785681
## absdiff.sqrt.age                0.06875455          0.143950560
##                       nodematch.race..wa.O absdiff.sqrt.age
## edges                          0.842483286       0.84390304
## nodefactor.deg.main.1          0.671971482       0.68902688
## nodefactor.race..wa.B          0.088613567       0.34356660
## nodefactor.race..wa.H          0.110720562       0.45917886
## nodefactor.region.EW           0.353493391       0.39247342
## nodefactor.region.OW           0.636478138       0.62043941
## concurrent                     0.794370299       0.80182799
## nodematch.race..wa.B           0.014759487       0.06875455
## nodematch.race..wa.H           0.006785681       0.14395056
## nodematch.race..wa.O           1.000000000       0.70950037
## absdiff.sqrt.age               0.709500368       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.037286150           0.053973070           0.083178684
## Lag 2e+05  0.023799557           0.024959716           0.024622302
## Lag 3e+05  0.002384504           0.006625950          -0.019009190
## Lag 4e+05  0.001583185          -0.002520842          -0.006808975
## Lag 5e+05 -0.014736850          -0.004921189          -0.019600725
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.064708674          0.025235989          0.024533318
## Lag 2e+05           0.010123800         -0.023004175          0.019214796
## Lag 3e+05           0.013486204         -0.009496313          0.009793142
## Lag 4e+05           0.005749365         -0.017052115          0.030921503
## Lag 5e+05           0.003898799          0.005610631         -0.019661396
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000         1.000000e+00          1.000000000
## Lag 1e+05  0.037093337         4.081248e-02          0.129533238
## Lag 2e+05  0.004351162         3.644228e-03          0.045049292
## Lag 3e+05  0.008055477         1.653099e-02          0.031994256
## Lag 4e+05  0.004681985        -1.177319e-03          0.015435097
## Lag 5e+05 -0.014824707        -7.333728e-06          0.008942917
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000
## Lag 1e+05           0.01814904      0.002137389
## Lag 2e+05           0.01712617      0.011427675
## Lag 3e+05           0.01077021     -0.018317130
## Lag 4e+05           0.01235665      0.011996843
## Lag 5e+05          -0.02694711      0.004830735
## Chain 2 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000          1.0000000000           1.000000000
## Lag 1e+05 0.029810042          0.0247548076           0.062691605
## Lag 2e+05 0.001266106          0.0033180904           0.015261145
## Lag 3e+05 0.036104133          0.0099935405           0.026363561
## Lag 4e+05 0.002473999         -0.0054341546          -0.005960574
## Lag 5e+05 0.004890703          0.0002319202          -0.005521103
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.043075754         0.0426253015          0.045429100
## Lag 2e+05           0.024111558        -0.0028820058         -0.008053735
## Lag 3e+05          -0.004511711         0.0154625712          0.036576616
## Lag 4e+05           0.001945730        -0.0112721333          0.006491514
## Lag 5e+05          -0.001830603         0.0001993954          0.001757440
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000           1.00000000           1.00000000
## Lag 1e+05  0.033181117           0.06751044           0.11669525
## Lag 2e+05 -0.002665167           0.00537341           0.01832427
## Lag 3e+05  0.031986078           0.01777450           0.01481518
## Lag 4e+05  0.008663433          -0.01545655           0.01465160
## Lag 5e+05  0.000642860          -0.01476121          -0.01128883
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.036241834      0.017711444
## Lag 2e+05          0.005172482      0.005439962
## Lag 3e+05          0.037093887      0.015267689
## Lag 4e+05          0.015096004      0.004385634
## Lag 5e+05          0.026496799      0.004792809
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.045994832           0.029266468           0.070185868
## Lag 2e+05  0.008519233          -0.005950597           0.022486965
## Lag 3e+05 -0.006237169          -0.007808244          -0.017282285
## Lag 4e+05  0.029173872           0.030815002          -0.010547668
## Lag 5e+05  0.013070976          -0.003110699          -0.005951494
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.063722769          0.024333436          0.024118209
## Lag 2e+05           0.035904931          0.009959550         -0.002207011
## Lag 3e+05           0.021359563          0.023246275         -0.005401745
## Lag 4e+05           0.006659831         -0.006364673          0.017542774
## Lag 5e+05          -0.005102857         -0.002660498          0.002294958
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.000000000          1.000000000          1.000000000
## Lag 1e+05 0.051655639          0.026600675          0.121305968
## Lag 2e+05 0.014867896          0.007572277          0.034444056
## Lag 3e+05 0.005346994          0.001751777         -0.005108472
## Lag 4e+05 0.030733697         -0.005029932          0.007177923
## Lag 5e+05 0.008712063         -0.005668931         -0.004320882
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000
## Lag 1e+05          0.028080372       0.01752561
## Lag 2e+05          0.013215180      -0.01276761
## Lag 3e+05         -0.008126697      -0.01992698
## Lag 4e+05          0.001219329       0.01060865
## Lag 5e+05          0.009198453       0.00631485
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.046979438           0.059636063           0.035060817
## Lag 2e+05 -0.009646163          -0.013474128           0.001079246
## Lag 3e+05  0.003323737          -0.011176088           0.019682855
## Lag 4e+05 -0.023260995          -0.028660818           0.028032824
## Lag 5e+05  0.007204432          -0.007629623           0.004375414
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000         1.000000e+00
## Lag 1e+05          0.0591349911          0.018943004         1.950200e-02
## Lag 2e+05         -0.0004480031         -0.002725787        -2.019611e-02
## Lag 3e+05         -0.0042947580          0.027014547         9.317803e-05
## Lag 4e+05         -0.0150846125         -0.007041113        -9.807235e-03
## Lag 5e+05          0.0048232534         -0.006120898         8.660884e-03
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000          1.000000000          1.000000000
## Lag 1e+05  0.0444343249          0.053761175          0.110710492
## Lag 2e+05 -0.0062045059         -0.013456352          0.032585937
## Lag 3e+05  0.0004976362         -0.011842201         -0.006273306
## Lag 4e+05 -0.0249906751         -0.003846662          0.007270896
## Lag 5e+05  0.0050188098         -0.010614518         -0.012300748
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.039635059     -0.000505848
## Lag 2e+05          0.009374158     -0.012724647
## Lag 3e+05          0.007849791     -0.014672800
## Lag 4e+05         -0.005207145     -0.014733071
## Lag 5e+05         -0.006241605      0.012576622
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.069829312           0.041938982            0.04184465
## Lag 2e+05 -0.005701886          -0.017335699            0.02578281
## Lag 3e+05  0.015282344          -0.006897152            0.01425194
## Lag 4e+05  0.009266438           0.029771024            0.01811135
## Lag 5e+05 -0.013286560          -0.009683505            0.01965237
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000           1.00000000
## Lag 1e+05           0.077564787          0.066482959           0.03675020
## Lag 2e+05           0.016836957          0.017137486          -0.01972807
## Lag 3e+05           0.012654103         -0.023918516           0.01438247
## Lag 4e+05           0.009452725         -0.008100835           0.03091076
## Lag 5e+05          -0.004527649          0.004439086          -0.00152464
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000         1.0000000000
## Lag 1e+05  0.066462975          0.044381718         0.1286658329
## Lag 2e+05 -0.013796907         -0.015332947         0.0221688051
## Lag 3e+05  0.006346225          0.022774605         0.0025354422
## Lag 4e+05 -0.001672189          0.009221063         0.0090288603
## Lag 5e+05 -0.010621236         -0.002448240        -0.0005961641
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.060382109      0.046926106
## Lag 2e+05         -0.016637481     -0.010075757
## Lag 3e+05          0.017354635      0.014745149
## Lag 4e+05          0.007179554      0.007226805
## Lag 5e+05         -0.016321773     -0.008523600
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.038116307          0.0256461226           0.050785980
## Lag 2e+05 -0.010911659         -0.0095280823          -0.012031487
## Lag 3e+05 -0.017010026          0.0001268855          -0.018595993
## Lag 4e+05  0.010620751          0.0122613897           0.006962936
## Lag 5e+05 -0.001511979          0.0024015302          -0.007845244
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.065610296          0.039242456          0.053204137
## Lag 2e+05           0.006331961          0.001917704          0.008586125
## Lag 3e+05           0.005067413         -0.015394618         -0.008686808
## Lag 4e+05          -0.008404839          0.010967022          0.009468107
## Lag 5e+05           0.012183647         -0.031621516         -0.020683961
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000          1.000000000         1.0000000000
## Lag 1e+05  0.0487649215          0.070290825         0.1385022836
## Lag 2e+05 -0.0153537173         -0.001692911         0.0145275078
## Lag 3e+05 -0.0026966517         -0.015234988         0.0105130579
## Lag 4e+05  0.0035381043         -0.011034684        -0.0080989193
## Lag 5e+05 -0.0007243528          0.011069148        -0.0002954398
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.036937039     -0.003971490
## Lag 2e+05          0.015793976      0.016749882
## Lag 3e+05         -0.007192037     -0.006522373
## Lag 4e+05         -0.009100066      0.011594334
## Lag 5e+05         -0.021438329      0.002404723
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.078280610           0.070802879          0.0328512933
## Lag 2e+05 -0.025087534          -0.019529753          0.0009767469
## Lag 3e+05  0.015938364           0.007005649         -0.0159548415
## Lag 4e+05  0.004576905          -0.012997840         -0.0101964851
## Lag 5e+05  0.015553809          -0.007698634         -0.0117533496
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05           0.097424481          0.057011262         0.0534904598
## Lag 2e+05           0.008153905          0.022433953        -0.0287604036
## Lag 3e+05           0.025298946          0.017477612         0.0009888269
## Lag 4e+05           0.003954406          0.003167544         0.0081740436
## Lag 5e+05          -0.013938974         -0.016393943         0.0318674514
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000           1.00000000          1.000000000
## Lag 1e+05  0.074061586           0.05705956          0.126969352
## Lag 2e+05 -0.023538252          -0.02992377          0.026454998
## Lag 3e+05  0.020286598          -0.00794342         -0.009863259
## Lag 4e+05 -0.001837354          -0.01674762         -0.016449983
## Lag 5e+05  0.001728648          -0.02388094         -0.005241626
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.049047685      0.040284976
## Lag 2e+05         -0.028003094     -0.009788137
## Lag 3e+05         -0.006704590      0.018804934
## Lag 4e+05          0.001765729      0.007836237
## Lag 5e+05          0.021771202      0.006542356
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.014492698           0.025203998           0.038957303
## Lag 2e+05 -0.001342423           0.009104508          -0.004216668
## Lag 3e+05 -0.010031467          -0.032178336           0.017529350
## Lag 4e+05 -0.017283374          -0.015368051           0.024523450
## Lag 5e+05  0.013102579           0.018108061           0.009978828
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05            0.06438740         -0.004589530          0.039568590
## Lag 2e+05            0.02702030          0.008517797          0.004413620
## Lag 3e+05           -0.01772278          0.010960372         -0.015493205
## Lag 4e+05           -0.03611078         -0.015312436         -0.004144681
## Lag 5e+05            0.04106608          0.006097113          0.023200539
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000          1.000000000
## Lag 1e+05  0.017398731          0.049210221          0.128279635
## Lag 2e+05  0.004721312          0.003032944          0.043719388
## Lag 3e+05 -0.009112096         -0.001167766          0.038539044
## Lag 4e+05 -0.016400998         -0.018298393         -0.008672095
## Lag 5e+05  0.004889841         -0.013433095          0.019190510
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05        -0.0005281401     -0.007672802
## Lag 2e+05        -0.0076028553      0.012468414
## Lag 3e+05         0.0154958465     -0.005035327
## Lag 4e+05        -0.0017618691     -0.006923323
## Lag 5e+05        -0.0203466309      0.018639543
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.95086              -0.85572              -2.35064 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.22592               0.05783              -0.28661 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.56666               0.45455               1.15390 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.44587              -0.04987 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.34167527            0.39215465            0.01874106 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.82126216            0.95388023            0.77440863 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.57094184            0.64943362            0.24854129 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.65569447            0.96022224 
## Joint P-value (lower = worse):  0.1724949 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.25537               0.23737               0.56534 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.07279              -0.52720               0.31301 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.53958              -0.32990              -1.75500 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.02794              -0.02963 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7984390             0.8123734             0.5718442 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9419732             0.5980539             0.7542742 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.5894876             0.7414735             0.0792598 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.9777087             0.9763628 
## Joint P-value (lower = worse):  0.8106185 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.8307               -1.9603               -0.4892 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.7056               -0.4852               -2.6789 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.4575               -0.5368                1.0163 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -1.9648               -1.9387 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.06714026            0.04996150            0.62467897 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.48042409            0.62753830            0.00738554 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.14498322            0.59139726            0.30949636 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.04943181            0.05253321 
## Joint P-value (lower = worse):  0.1388281 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.54246               0.01329               0.75142 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.19336               0.51226               0.54583 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.73670              -2.05268              -0.72858 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.32310               0.44429 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.58750075            0.98939541            0.45240186 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.84667823            0.60846775            0.58518139 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.46130479            0.04010362            0.46625914 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.74661640            0.65683298 
## Joint P-value (lower = worse):  0.5998682 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.7703               -0.7951               -0.1447 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.4544                1.0915               -0.6894 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.0161               -1.7074                1.3674 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -1.0228                0.1336 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.44114243            0.42658118            0.88491260 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.64953547            0.27506236            0.49057987 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.98715684            0.08774392            0.17150397 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.30639226            0.89370692 
## Joint P-value (lower = worse):  0.1115582 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.7432               -0.4806               -1.8145 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.3513                0.4665                0.3618 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.4552               -1.6340                0.3979 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -0.4570               -0.5523 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.45735013            0.63080676            0.06959447 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.72533686            0.64084048            0.71747586 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.64897426            0.10226746            0.69068901 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.64768824            0.58074698 
## Joint P-value (lower = worse):  0.5455631 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.94331               1.51127              -0.07321 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.59393              -0.30278               0.77654 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.92448              -0.17841              -0.26814 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               1.48486               1.31954 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3455200             0.1307198             0.9416429 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5525599             0.7620547             0.4374317 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.3552371             0.8584000             0.7885940 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.1375820             0.1869872 
## Joint P-value (lower = worse):  0.8577423 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3334               -0.3222               -1.0021 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.3851               -1.0456               -0.1218 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.5332                0.3746               -0.2185 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                0.1865                0.2666 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7388402             0.7473110             0.3163088 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.7001695             0.2957361             0.9030921 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.5939285             0.7079896             0.8270315 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.8520545             0.7897682 
## Joint P-value (lower = worse):  0.9469402 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 8

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  0.66680 58.448  0.33745        0.36719
## nodefactor.deg.main.1 -0.28323 60.598  0.34987        0.39071
## nodefactor.race..wa.B  0.45133 19.551  0.11288        0.12474
## nodefactor.race..wa.H -0.40497 29.565  0.17069        0.20151
## nodefactor.region.EW  -0.35553 29.095  0.16798        0.22619
## nodefactor.region.OW   1.52177 58.429  0.33734        0.38532
## concurrent             0.65537 52.479  0.30299        0.33920
## nodematch.race..wa.B   0.01225  2.959  0.01709        0.01936
## nodematch.race..wa.H  -0.11196  7.367  0.04254        0.05847
## nodematch.race..wa.O   0.62632 44.390  0.25629        0.28304
## nodematch.region       0.79673 50.110  0.28931        0.32255
## absdiff.sqrt.age       0.23514 57.200  0.33024        0.34861
## 
## 2. Quantiles for each variable:
## 
##                          2.5%     25%      50%    75%  97.5%
## edges                 -113.50 -38.500  0.50000 40.500 115.50
## nodefactor.deg.main.1 -117.00 -41.000  0.00000 40.000 120.00
## nodefactor.race..wa.B  -37.52 -12.517  0.48320 13.483  39.48
## nodefactor.race..wa.H  -59.34 -20.340 -0.34000 19.660  57.66
## nodefactor.region.EW   -56.59 -20.588 -0.58850 19.412  58.41
## nodefactor.region.OW  -112.25 -38.255  0.74500 40.745 115.75
## concurrent            -101.00 -35.000  0.00000 36.000 104.00
## nodematch.race..wa.B    -5.48  -2.480 -0.47985  1.520   6.52
## nodematch.race..wa.H   -14.18  -5.181 -0.18150  4.819  14.82
## nodematch.race..wa.O   -86.08 -29.081 -0.08078 29.919  88.92
## nodematch.region       -97.00 -33.000  1.00000 34.000  99.00
## absdiff.sqrt.age      -111.00 -38.218 -0.42208 38.203 114.40
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.81456784
## nodefactor.deg.main.1 0.81456784            1.00000000
## nodefactor.race..wa.B 0.40935667            0.30881917
## nodefactor.race..wa.H 0.53767302            0.47318457
## nodefactor.region.EW  0.38372755            0.31293465
## nodefactor.region.OW  0.61476344            0.44840452
## concurrent            0.95340527            0.77306159
## nodematch.race..wa.B  0.07765574            0.05024095
## nodematch.race..wa.H  0.16467644            0.15594565
## nodematch.race..wa.O  0.84442801            0.67789983
## nodematch.region      0.93038530            0.76251573
## absdiff.sqrt.age      0.84574946            0.68769371
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.40935667            0.53767302
## nodefactor.deg.main.1            0.30881917            0.47318457
## nodefactor.race..wa.B            1.00000000            0.18166470
## nodefactor.race..wa.H            0.18166470            1.00000000
## nodefactor.region.EW             0.09764293            0.34586107
## nodefactor.region.OW             0.21743022            0.31827692
## concurrent                       0.39614937            0.52278941
## nodematch.race..wa.B             0.36437062            0.01266853
## nodematch.race..wa.H             0.02020527            0.56056088
## nodematch.race..wa.O             0.09229356            0.11163319
## nodematch.region                 0.39016704            0.48281919
## absdiff.sqrt.age                 0.34885504            0.45685588
##                       nodefactor.region.EW nodefactor.region.OW concurrent
## edges                          0.383727554           0.61476344 0.95340527
## nodefactor.deg.main.1          0.312934654           0.44840452 0.77306159
## nodefactor.race..wa.B          0.097642931           0.21743022 0.39614937
## nodefactor.race..wa.H          0.345861071           0.31827692 0.52278941
## nodefactor.region.EW           1.000000000           0.11159336 0.36057873
## nodefactor.region.OW           0.111593364           1.00000000 0.57699309
## concurrent                     0.360578729           0.57699309 1.00000000
## nodematch.race..wa.B           0.006754638           0.03740027 0.07665564
## nodematch.race..wa.H           0.164992910           0.10041914 0.16434475
## nodematch.race..wa.O           0.273051536           0.53839931 0.79818692
## nodematch.region               0.253396240           0.53932461 0.88909544
## absdiff.sqrt.age               0.325989762           0.52057949 0.80432294
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                          0.077655738          0.164676442
## nodefactor.deg.main.1          0.050240951          0.155945653
## nodefactor.race..wa.B          0.364370624          0.020205271
## nodefactor.race..wa.H          0.012668525          0.560560879
## nodefactor.region.EW           0.006754638          0.164992910
## nodefactor.region.OW           0.037400274          0.100419144
## concurrent                     0.076655640          0.164344753
## nodematch.race..wa.B           1.000000000          0.002924091
## nodematch.race..wa.H           0.002924091          1.000000000
## nodematch.race..wa.O           0.006223547          0.003378771
## nodematch.region               0.073980255          0.143059365
## absdiff.sqrt.age               0.064027496          0.137716482
##                       nodematch.race..wa.O nodematch.region
## edges                          0.844428009       0.93038530
## nodefactor.deg.main.1          0.677899825       0.76251573
## nodefactor.race..wa.B          0.092293565       0.39016704
## nodefactor.race..wa.H          0.111633191       0.48281919
## nodefactor.region.EW           0.273051536       0.25339624
## nodefactor.region.OW           0.538399314       0.53932461
## concurrent                     0.798186920       0.88909544
## nodematch.race..wa.B           0.006223547       0.07398025
## nodematch.race..wa.H           0.003378771       0.14305937
## nodematch.race..wa.O           1.000000000       0.79153679
## nodematch.region               0.791536788       1.00000000
## absdiff.sqrt.age               0.711535111       0.78584345
##                       absdiff.sqrt.age
## edges                        0.8457495
## nodefactor.deg.main.1        0.6876937
## nodefactor.race..wa.B        0.3488550
## nodefactor.race..wa.H        0.4568559
## nodefactor.region.EW         0.3259898
## nodefactor.region.OW         0.5205795
## concurrent                   0.8043229
## nodematch.race..wa.B         0.0640275
## nodematch.race..wa.H         0.1377165
## nodematch.race..wa.O         0.7115351
## nodematch.region             0.7858435
## absdiff.sqrt.age             1.0000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.074293832           0.103184510           0.099417520
## Lag 2e+05  0.003235864           0.007750902           0.032188678
## Lag 3e+05  0.019147412           0.009635380           0.006955118
## Lag 4e+05  0.017612327           0.027764309           0.011395681
## Lag 5e+05 -0.006474480          -0.007160272          -0.023443624
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05            0.14509585          0.207746950          0.136487350
## Lag 2e+05            0.03518996          0.084236194          0.030927399
## Lag 3e+05            0.03735473          0.042162927          0.016833797
## Lag 4e+05           -0.01440172         -0.003179383          0.006021293
## Lag 5e+05           -0.01985848         -0.017586317          0.007254426
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000          1.000000000
## Lag 1e+05  0.085703515          0.101190378          0.232560491
## Lag 2e+05  0.011717952          0.014873165          0.085772228
## Lag 3e+05  0.006315926         -0.017897670          0.050480378
## Lag 4e+05  0.007808545          0.001998121         -0.004240963
## Lag 5e+05 -0.003418756         -0.012928698          0.012982839
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.073623014      0.108060165      0.013346907
## Lag 2e+05          0.012916930      0.027019565      0.003174847
## Lag 3e+05         -0.009078349      0.016055797      0.004733757
## Lag 4e+05          0.023761772     -0.005430556      0.006818297
## Lag 5e+05          0.002785007      0.002187026     -0.017992580
## Chain 2 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000           1.000000000          1.0000000000
## Lag 1e+05 0.115223816           0.125460243          0.1193641005
## Lag 2e+05 0.009324493           0.025916692          0.0154064194
## Lag 3e+05 0.012966545           0.014875380         -0.0009208817
## Lag 4e+05 0.024082652           0.005666413          0.0077636945
## Lag 5e+05 0.032722502          -0.003934597          0.0180051689
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000           1.00000000          1.000000000
## Lag 1e+05            0.15064462           0.22376202          0.152869891
## Lag 2e+05            0.04103333           0.11661253          0.017991300
## Lag 3e+05            0.02424551           0.04612830         -0.005044468
## Lag 4e+05            0.01232480           0.03524642          0.012214286
## Lag 5e+05            0.01802221           0.03528585          0.011701918
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.000000000           1.00000000           1.00000000
## Lag 1e+05 0.125260272           0.08480411           0.24172748
## Lag 2e+05 0.009617682           0.03928150           0.09046472
## Lag 3e+05 0.008979788          -0.01032875           0.04097252
## Lag 4e+05 0.024174416          -0.01563746           0.04167515
## Lag 5e+05 0.038257803          -0.02714986           0.04166747
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.102373798      0.130918465      0.070129431
## Lag 2e+05          0.005066625     -0.002073079      0.002905888
## Lag 3e+05          0.006226339      0.022411360     -0.011380755
## Lag 4e+05          0.038630460      0.016027926      0.010600987
## Lag 5e+05          0.054250347      0.030869334      0.011872936
## Chain 3 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000           1.000000000           1.000000000
## Lag 1e+05 0.087882032           0.091039267           0.121189900
## Lag 2e+05 0.020945280           0.041463336           0.021004095
## Lag 3e+05 0.004190995           0.010480954          -0.028531850
## Lag 4e+05 0.003060805          -0.006696759          -0.007880029
## Lag 5e+05 0.021879214           0.021980128          -0.005641812
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.131896432           0.22849262          0.104251287
## Lag 2e+05           0.045359576           0.10513004          0.015184035
## Lag 3e+05          -0.003647636           0.08003565         -0.035096920
## Lag 4e+05           0.010741760           0.03931201         -0.003852028
## Lag 5e+05           0.037280635           0.02563615         -0.012858144
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.102314640          0.077471881           0.28060387
## Lag 2e+05  0.034863788          0.033003690           0.09379429
## Lag 3e+05  0.010830524          0.003756270           0.03919141
## Lag 4e+05 -0.007357862          0.006302403           0.04841501
## Lag 5e+05  0.020597082          0.005428096           0.05800565
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000     1.0000000000
## Lag 1e+05          0.069197071      0.120046628     0.0428075022
## Lag 2e+05          0.017875164      0.015370300     0.0238138148
## Lag 3e+05         -0.004151606     -0.004578317    -0.0100426214
## Lag 4e+05         -0.011063107      0.016452838    -0.0062286812
## Lag 5e+05          0.003506228      0.015527336    -0.0002153367
## Chain 4 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.09965993           0.085906354           0.095825777
## Lag 2e+05  0.02288362           0.013038705           0.027394229
## Lag 3e+05  0.01500735           0.037029784          -0.002300478
## Lag 4e+05 -0.01560653          -0.022260062           0.008738308
## Lag 5e+05  0.01242791          -0.001170029           0.009693686
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000           1.00000000         1.0000000000
## Lag 1e+05            0.17597384           0.23764473         0.1184861441
## Lag 2e+05            0.05671462           0.12191994         0.0214990675
## Lag 3e+05            0.02751840           0.04664070         0.0135271955
## Lag 4e+05            0.02177744           0.04419933        -0.0006331608
## Lag 5e+05            0.01959448           0.03671348         0.0257683669
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.112858636          0.094418845           0.26739887
## Lag 2e+05  0.021544028          0.056460475           0.08951689
## Lag 3e+05  0.013182985          0.006552299           0.05954811
## Lag 4e+05 -0.012851059          0.001760424           0.05222515
## Lag 5e+05  0.002607713         -0.033753878           0.01679016
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000     1.000000e+00
## Lag 1e+05           0.07490300      0.126989905     6.148455e-02
## Lag 2e+05           0.01575161      0.026939046     2.384938e-02
## Lag 3e+05           0.01368416      0.026676327     6.261880e-03
## Lag 4e+05          -0.01814433     -0.007087429    -6.825193e-05
## Lag 5e+05           0.01411661      0.009143597     4.661058e-03
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.095829257           0.099756248            0.08681252
## Lag 2e+05  0.013053650           0.009667583           -0.02029464
## Lag 3e+05 -0.012695800          -0.001958401            0.01147690
## Lag 4e+05 -0.015301872          -0.008372395           -0.01105644
## Lag 5e+05  0.005846601           0.007889433           -0.01003685
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000           1.00000000
## Lag 1e+05           0.149022359           0.21477377           0.12964445
## Lag 2e+05           0.058485212           0.10335735           0.03305176
## Lag 3e+05           0.023253319           0.05815442          -0.02474144
## Lag 4e+05          -0.005692117           0.01785135          -0.03134798
## Lag 5e+05           0.023873202           0.01607740          -0.01074687
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.108027150          0.120269981           0.22619595
## Lag 2e+05  0.016543501          0.014070368           0.07723189
## Lag 3e+05  0.003252081         -0.022820270           0.03353427
## Lag 4e+05 -0.001535082          0.006914825           0.02678001
## Lag 5e+05  0.008407682         -0.025428426           0.01978278
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000      1.000000000
## Lag 1e+05          0.100896695       0.13316347      0.062489591
## Lag 2e+05          0.013927347       0.01350103      0.004510762
## Lag 3e+05         -0.004561992      -0.02151337     -0.006091791
## Lag 4e+05         -0.003427188      -0.02026785     -0.005896213
## Lag 5e+05         -0.006602846      -0.00211587      0.015244832
## Chain 6 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000           1.000000000           1.000000000
## Lag 1e+05 0.110822089           0.104475352           0.116854618
## Lag 2e+05 0.032670686           0.037658351           0.041640538
## Lag 3e+05 0.012880414           0.017445646           0.009049369
## Lag 4e+05 0.006163556          -0.012091604          -0.022953106
## Lag 5e+05 0.018532449           0.002957128           0.016554608
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.149949333           0.24470239          0.144931494
## Lag 2e+05           0.043272434           0.11025784          0.024020775
## Lag 3e+05           0.016379884           0.08454198          0.006030983
## Lag 4e+05           0.008830322           0.04888941          0.002409433
## Lag 5e+05           0.011196342          -0.01007933          0.017713875
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.000000000          1.000000000           1.00000000
## Lag 1e+05 0.118290139          0.130864654           0.24216763
## Lag 2e+05 0.028463444          0.022262901           0.08805779
## Lag 3e+05 0.016152060         -0.005293691           0.05253961
## Lag 4e+05 0.009733243         -0.005783728           0.03662347
## Lag 5e+05 0.009080203         -0.006199656           0.02953350
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000     1.0000000000
## Lag 1e+05          0.090577207       0.12445116     0.0539571400
## Lag 2e+05          0.031937051       0.03135497    -0.0158199301
## Lag 3e+05          0.006908005       0.02599254     0.0119028066
## Lag 4e+05          0.028890178       0.00485657     0.0006412076
## Lag 5e+05          0.006978902       0.02268425     0.0102243027
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.119655203            0.11629333           0.096200024
## Lag 2e+05  0.012186792            0.02028062          -0.005461918
## Lag 3e+05 -0.007695295           -0.01257345           0.008225973
## Lag 4e+05 -0.021553244           -0.02948419          -0.014846870
## Lag 5e+05 -0.018705821           -0.02577632          -0.006179736
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000           1.00000000
## Lag 1e+05            0.15048807          0.248343014           0.16286658
## Lag 2e+05            0.03528786          0.076380143           0.05595228
## Lag 3e+05           -0.02799785          0.042653199           0.03046889
## Lag 4e+05           -0.03213655          0.016475017           0.02174683
## Lag 5e+05           -0.02936321          0.009942162           0.01197120
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000          1.000000000
## Lag 1e+05  0.119063415          0.078827405          0.223165480
## Lag 2e+05  0.023225961          0.035913827          0.084158034
## Lag 3e+05 -0.002429621          0.008113151          0.015522006
## Lag 4e+05 -0.019807585         -0.014222378          0.006265332
## Lag 5e+05 -0.020237895          0.010747700          0.001661586
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000     1.0000000000
## Lag 1e+05          0.081664055       0.13986094     0.0713530831
## Lag 2e+05         -0.001422855       0.01114823    -0.0001825523
## Lag 3e+05         -0.004998221      -0.02907384     0.0034548259
## Lag 4e+05         -0.004114214      -0.03444100    -0.0214795508
## Lag 5e+05          0.007288660      -0.02503555    -0.0160214387
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.093343037           0.078285568           0.078901600
## Lag 2e+05  0.040636377           0.027985531           0.016820382
## Lag 3e+05  0.001246276           0.023158699           0.005714428
## Lag 4e+05 -0.005815376          -0.008688396          -0.007131129
## Lag 5e+05 -0.007561410          -0.003487521          -0.010611769
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.132503848          0.231695399          0.134358726
## Lag 2e+05           0.040672340          0.097945003          0.013834241
## Lag 3e+05          -0.013528752          0.019039257          0.002100438
## Lag 4e+05          -0.004497829          0.004624715          0.000404643
## Lag 5e+05           0.010497644          0.013923770          0.004361277
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.102417511          0.078739115           0.22207289
## Lag 2e+05  0.028443227          0.044502596           0.09783127
## Lag 3e+05  0.001307577          0.001499173           0.03131178
## Lag 4e+05 -0.007274084          0.032452400           0.01774579
## Lag 5e+05 -0.010498868          0.033350757           0.01915487
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000      1.000000000
## Lag 1e+05          0.089375846       0.12369334      0.064247760
## Lag 2e+05          0.045304306       0.05186764      0.014606909
## Lag 3e+05         -0.013031036       0.01682722      0.017017466
## Lag 4e+05         -0.006798134      -0.01323113      0.015966310
## Lag 5e+05         -0.021934717      -0.01782833      0.007989782
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.4609               -1.0715                0.1384 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.1733               -0.2985               -0.9342 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.2804                1.9406               -1.4466 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               -0.5649               -0.8217               -0.6377 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.64487253            0.28395647            0.88993828 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.86245326            0.76530043            0.35019126 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.77918346            0.05230778            0.14801322 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.57212503            0.41125800            0.52369479 
## Joint P-value (lower = worse):  0.3054153 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.7290                1.3571               -0.1744 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                1.1600                0.4552                0.8827 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.8108                0.9515               -0.3113 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##                1.2210                1.5909                1.2676 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.08380489            0.17475429            0.86155823 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.24604230            0.64894109            0.37741861 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.07017914            0.34132868            0.75555829 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.22209196            0.11162970            0.20494271 
## Joint P-value (lower = worse):  0.6623661 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.6686                0.6608                1.4712 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.5310               -0.1524                0.1810 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.9611                0.7799               -0.1682 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##                0.2012                0.5015               -0.3641 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5037695             0.5087711             0.1412246 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5954229             0.8789045             0.8563528 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.3365032             0.4354553             0.8664026 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.8405243             0.6160452             0.7157900 
## Joint P-value (lower = worse):  0.8041529 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.9484               -1.5226                0.9036 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.1150               -0.9920               -0.7184 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.3198                0.8713               -0.6261 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               -0.7878               -1.0664               -1.3658 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3429310             0.1278636             0.3661999 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.2648709             0.3212203             0.4725249 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.1868865             0.3835701             0.5312710 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.4308254             0.2862503             0.1720174 
## Joint P-value (lower = worse):  0.637911 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.2413               -0.5338               -1.1720 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -2.0669               -1.1867                0.7389 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.6866               -0.4951               -1.7282 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##                0.8545               -0.6194                0.1275 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.80935013            0.59350570            0.24117757 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.03874792            0.23535837            0.45999548 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.49235589            0.62049989            0.08395362 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.39280043            0.53566661            0.89855936 
## Joint P-value (lower = worse):  0.380919 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.78487               0.50761               0.40398 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.81492               0.07568               0.96900 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.85771               0.25023               0.62921 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               0.31261               0.50497               1.23779 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.4325296             0.6117286             0.6862299 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.4151166             0.9396727             0.3325436 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.3910545             0.8024123             0.5292140 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.7545755             0.6135768             0.2157949 
## Joint P-value (lower = worse):  0.9785587 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.8311                0.6403                0.5042 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.8659                0.3932                1.6586 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.7850               -2.2687                1.2192 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##                0.1300                0.5541                0.9714 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.40594493            0.52199430            0.61409291 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.38654681            0.69414037            0.09720191 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.43242436            0.02328945            0.22278489 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.89658071            0.57954032            0.33133755 
## Joint P-value (lower = worse):  0.2502095 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.27638              -0.38847               0.40707 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.28886               0.65683              -1.28733 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.06285              -0.46211              -0.23880 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -0.79642              -0.53646              -1.28862 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7822535             0.6976688             0.6839559 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.7726886             0.5112927             0.1979791 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9498845             0.6440026             0.8112637 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.4257883             0.5916400             0.1975302 
## Joint P-value (lower = worse):  0.17804 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.p.buildup.bal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55ba04e08fb0>
## 
## Iterations:  80 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -9.92022    0.02474      0  <1e-04 ***
## deg3+                      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.I     -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R     -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 2

summary(est.p.buildup.bal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55ba28d65d08>
## 
## Iterations:  86 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC %  p-value    
## edges                  -10.06381    0.03026      0  < 1e-04 ***
## nodefactor.race..wa.B    0.24745    0.06626      0 0.000188 ***
## nodefactor.race..wa.H    0.45289    0.04864      0  < 1e-04 ***
## deg3+                       -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 3

summary(est.p.buildup.bal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55ba46ecf200>
## 
## Iterations:  95 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -10.5470     0.1530      0 < 1e-04 ***
## nodefactor.race..wa.B    0.6620     0.1380      0 < 1e-04 ***
## nodefactor.race..wa.H    0.8720     0.1462      0 < 1e-04 ***
## nodematch.race..wa.B    -0.5210     0.3745      0 0.16413    
## nodematch.race..wa.H    -0.2335     0.2066      0 0.25853    
## nodematch.race..wa.O     0.5010     0.1550      0 0.00122 ** 
## deg3+                      -Inf     0.0000      0 < 1e-04 ***
## nodematch.role.class.I     -Inf     0.0000      0 < 1e-04 ***
## nodematch.role.class.R     -Inf     0.0000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 4

summary(est.p.buildup.bal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55ba6519a210>
## 
## Iterations:  99 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -10.42309    0.15666      0 < 1e-04 ***
## nodefactor.deg.main.1   -0.14241    0.03402      0 < 1e-04 ***
## nodefactor.race..wa.B    0.64936    0.13959      0 < 1e-04 ***
## nodefactor.race..wa.H    0.88531    0.14768      0 < 1e-04 ***
## nodematch.race..wa.B    -0.52073    0.37622      0 0.16632    
## nodematch.race..wa.H    -0.23081    0.20857      0 0.26845    
## nodematch.race..wa.O     0.49933    0.15653      0 0.00142 ** 
## deg3+                       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 5

summary(est.p.buildup.bal[[5]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55ba8352dc20>
## 
## Iterations:  83 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -10.22601    0.15921      0 < 1e-04 ***
## nodefactor.deg.main.1   -0.16025    0.03412      0 < 1e-04 ***
## nodefactor.race..wa.B    0.62238    0.13904      0 < 1e-04 ***
## nodefactor.race..wa.H    0.90832    0.14718      0 < 1e-04 ***
## nodefactor.region.EW    -0.25682    0.05965      0 < 1e-04 ***
## nodefactor.region.OW    -0.21613    0.03732      0 < 1e-04 ***
## nodematch.race..wa.B    -0.52022    0.37640      0 0.16694    
## nodematch.race..wa.H    -0.23307    0.20954      0 0.26602    
## nodematch.race..wa.O     0.50157    0.15610      0 0.00131 ** 
## deg3+                       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 6

summary(est.p.buildup.bal[[6]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55baa19f3c40>
## 
## Iterations:  87 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -9.67402    0.16039      0 < 1e-04 ***
## nodefactor.deg.main.1  -0.15920    0.03398      0 < 1e-04 ***
## nodefactor.race..wa.B   0.62469    0.13884      0 < 1e-04 ***
## nodefactor.race..wa.H   0.90777    0.14674      0 < 1e-04 ***
## nodefactor.region.EW   -0.25784    0.05961      0 < 1e-04 ***
## nodefactor.region.OW   -0.21543    0.03763      0 < 1e-04 ***
## nodematch.race..wa.B   -0.51827    0.37492      0 0.16686    
## nodematch.race..wa.H   -0.23231    0.20782      0 0.26363    
## nodematch.race..wa.O    0.50048    0.15516      0 0.00126 ** 
## absdiff.sqrt.age       -0.56588    0.03254      0 < 1e-04 ***
## deg3+                      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I     -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R     -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 7

summary(est.p.buildup.bal[[7]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + concurrent + 
##     nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") + 
##     degrange(from = 3) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x55babff89fe0>
## 
## Iterations:  89 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC %  p-value    
## edges                  -11.54354    0.16846      0  < 1e-04 ***
## nodefactor.deg.main.1   -0.11233    0.02875      0  < 1e-04 ***
## nodefactor.race..wa.B    0.56471    0.13504      0  < 1e-04 ***
## nodefactor.race..wa.H    0.76359    0.14550      0  < 1e-04 ***
## nodefactor.region.EW    -0.18174    0.04967      0 0.000253 ***
## nodefactor.region.OW    -0.15146    0.03175      0  < 1e-04 ***
## concurrent               2.49735    0.06359      0  < 1e-04 ***
## nodematch.race..wa.B    -0.52043    0.37590      0 0.166211    
## nodematch.race..wa.H    -0.23235    0.20670      0 0.260981    
## nodematch.race..wa.O     0.50042    0.15633      0 0.001369 ** 
## absdiff.sqrt.age        -0.54168    0.03245      0  < 1e-04 ***
## deg3+                       -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 8

summary(est.p.buildup.bal[[8]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + concurrent + 
##     nodematch("race..wa", diff = TRUE) + nodematch("region", 
##     diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55bade5e6250>
## 
## Iterations:  82 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC %  p-value    
## edges                  -13.03488    0.17484      0  < 1e-04 ***
## nodefactor.deg.main.1   -0.11231    0.02877      0  < 1e-04 ***
## nodefactor.race..wa.B    0.59283    0.13447      0  < 1e-04 ***
## nodefactor.race..wa.H    0.80083    0.14493      0  < 1e-04 ***
## nodefactor.region.EW     0.52492    0.04078      0  < 1e-04 ***
## nodefactor.region.OW     0.14806    0.02260      0  < 1e-04 ***
## concurrent               2.49753    0.06334      0  < 1e-04 ***
## nodematch.race..wa.B    -0.58084    0.37503      0 0.121433    
## nodematch.race..wa.H    -0.31948    0.20560      0 0.120206    
## nodematch.race..wa.O     0.53075    0.15580      0 0.000658 ***
## nodematch.region         1.79933    0.05810      0  < 1e-04 ***
## absdiff.sqrt.age        -0.54144    0.03277      0  < 1e-04 ***
## deg3+                       -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Network diagnostics

Model 1

(dx_pers1 <- netdx(est.p.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                        Target Sim Mean Pct Diff Sim SD
## edges                  2017.5 2059.669    0.021 42.921
## nodefactor.deg.main.1      NA 1846.935       NA 48.715
## nodefactor.race..wa.B      NA  253.799       NA 15.857
## nodefactor.race..wa.H      NA  445.554       NA 21.142
## nodefactor.region.EW       NA  415.736       NA 20.061
## nodefactor.region.OW       NA 1347.160       NA 38.175
## concurrent                 NA  628.469       NA 29.127
## nodematch.race..wa.B       NA    7.826       NA  2.652
## nodematch.race..wa.H       NA   24.191       NA  4.735
## nodematch.race..wa.O       NA 1419.178       NA 34.707
## nodematch.region           NA  915.952       NA 27.341
## absdiff.sqrt.age           NA 2343.312       NA 60.823
## deg3+                      NA    0.000       NA  0.000
## nodematch.role.class.I     NA    0.000       NA  0.000
## nodematch.role.class.R     NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.663   -0.029 30.141
## Pct Edges Diss  0.032    0.032   -0.002  0.004
plot(dx_pers1, type="formation")

plot(dx_pers1, type="duration")

plot(dx_pers1, type="dissolution")

Model 2

(dx_pers2 <- netdx(est.p.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2060.726    0.021 40.062
## nodefactor.deg.main.1        NA 1867.543       NA 47.468
## nodefactor.race..wa.B   285.517  290.728    0.018 15.759
## nodefactor.race..wa.H   605.340  616.057    0.018 24.265
## nodefactor.region.EW         NA  435.364       NA 20.380
## nodefactor.region.OW         NA 1335.239       NA 37.705
## concurrent                   NA  636.602       NA 26.910
## nodematch.race..wa.B         NA   10.361       NA  3.159
## nodematch.race..wa.H         NA   45.831       NA  6.540
## nodematch.race..wa.O         NA 1254.134       NA 32.252
## nodematch.region             NA  908.173       NA 28.712
## absdiff.sqrt.age             NA 2346.623       NA 57.130
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.631   -0.030 30.105
## Pct Edges Diss  0.032    0.032   -0.002  0.004
plot(dx_pers2, type="formation")

plot(dx_pers2, type="duration")

plot(dx_pers2, type="dissolution")

Model 3

(dx_pers3 <- netdx(est.p.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2055.890    0.019 40.837
## nodefactor.deg.main.1        NA 1854.729       NA 47.055
## nodefactor.race..wa.B   285.517  291.034    0.019 16.287
## nodefactor.race..wa.H   605.340  613.862    0.014 23.542
## nodefactor.region.EW         NA  431.440       NA 19.738
## nodefactor.region.OW         NA 1335.219       NA 39.201
## concurrent                   NA  633.098       NA 28.292
## nodematch.race..wa.B      8.480    8.407   -0.009  2.802
## nodematch.race..wa.H     51.181   51.494    0.006  6.920
## nodematch.race..wa.O   1247.081 1272.664    0.021 33.442
## nodematch.region             NA  909.506       NA 27.461
## absdiff.sqrt.age             NA 2342.499       NA 59.039
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.548   -0.032 30.049
## Pct Edges Diss  0.032    0.032    0.001  0.004
plot(dx_pers3, type="formation")

plot(dx_pers3, type="duration")

plot(dx_pers3, type="dissolution")

Model 4

(dx_pers4 <- netdx(est.p.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2061.070    0.022 42.153
## nodefactor.deg.main.1  1699.000 1735.776    0.022 47.901
## nodefactor.race..wa.B   285.517  289.416    0.014 16.027
## nodefactor.race..wa.H   605.340  614.413    0.015 23.673
## nodefactor.region.EW         NA  431.503       NA 20.768
## nodefactor.region.OW         NA 1346.195       NA 40.288
## concurrent                   NA  637.361       NA 29.811
## nodematch.race..wa.B      8.480    8.554    0.009  3.069
## nodematch.race..wa.H     51.181   51.323    0.003  6.948
## nodematch.race..wa.O   1247.081 1278.867    0.025 33.885
## nodematch.region             NA  908.921       NA 27.929
## absdiff.sqrt.age             NA 2346.743       NA 63.112
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.628   -0.030 30.099
## Pct Edges Diss  0.032    0.032   -0.002  0.004
plot(dx_pers4, type="formation")

plot(dx_pers4, type="duration")

plot(dx_pers4, type="dissolution")

Model 5

(dx_pers5 <- netdx(est.p.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2055.782    0.019 41.873
## nodefactor.deg.main.1  1699.000 1732.374    0.020 46.514
## nodefactor.race..wa.B   285.517  289.460    0.014 15.342
## nodefactor.race..wa.H   605.340  615.952    0.018 24.579
## nodefactor.region.EW    367.588  376.035    0.023 19.451
## nodefactor.region.OW   1182.255 1202.559    0.017 37.526
## concurrent                   NA  637.673       NA 29.160
## nodematch.race..wa.B      8.480    8.644    0.019  2.783
## nodematch.race..wa.H     51.181   52.035    0.017  6.983
## nodematch.race..wa.O   1247.081 1271.981    0.020 35.816
## nodematch.region             NA  971.115       NA 30.757
## absdiff.sqrt.age             NA 2340.434       NA 59.009
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.621   -0.030 30.086
## Pct Edges Diss  0.032    0.032   -0.001  0.004
plot(dx_pers5, type="formation")

plot(dx_pers5, type="duration")

plot(dx_pers5, type="dissolution")

Model 6

(dx_pers6 <- netdx(est.p.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2050.562    0.016 40.344
## nodefactor.deg.main.1  1699.000 1726.555    0.016 46.411
## nodefactor.race..wa.B   285.517  288.219    0.009 16.881
## nodefactor.race..wa.H   605.340  611.583    0.010 23.079
## nodefactor.region.EW    367.588  374.534    0.019 18.886
## nodefactor.region.OW   1182.255 1202.498    0.017 36.476
## concurrent                   NA  640.420       NA 28.098
## nodematch.race..wa.B      8.480    8.793    0.037  2.977
## nodematch.race..wa.H     51.181   51.542    0.007  6.957
## nodematch.race..wa.O   1247.081 1271.470    0.020 34.569
## nodematch.region             NA  967.810       NA 28.322
## absdiff.sqrt.age       1664.841 1687.014    0.013 45.283
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.547   -0.032 30.074
## Pct Edges Diss  0.032    0.032    0.001  0.004
plot(dx_pers6, type="formation")

plot(dx_pers6, type="duration")

plot(dx_pers6, type="dissolution")

Model 7

(dx_pers7 <- netdx(est.p.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2070.715    0.026 65.110
## nodefactor.deg.main.1  1699.000 1763.286    0.038 66.290
## nodefactor.race..wa.B   285.517  286.895    0.005 19.654
## nodefactor.race..wa.H   605.340  596.049   -0.015 29.406
## nodefactor.region.EW    367.588  383.522    0.043 23.015
## nodefactor.region.OW   1182.255 1229.035    0.040 51.954
## concurrent             1384.000 1344.087   -0.029 56.113
## nodematch.race..wa.B      8.480    9.025    0.064  3.168
## nodematch.race..wa.H     51.181   46.022   -0.101  6.456
## nodematch.race..wa.O   1247.081 1296.576    0.040 47.250
## nodematch.region             NA  973.100       NA 39.374
## absdiff.sqrt.age       1664.841 1785.756    0.073 69.978
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.645   -0.029 30.074
## Pct Edges Diss  0.032    0.032   -0.002  0.004
plot(dx_pers7, type="formation")

plot(dx_pers7, type="duration")

plot(dx_pers7, type="dissolution")

Model 8

(dx_pers8 <- netdx(est.p.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2005.237   -0.006 62.821
## nodefactor.deg.main.1  1699.000 1706.711    0.005 60.760
## nodefactor.race..wa.B   285.517  280.520   -0.018 19.426
## nodefactor.race..wa.H   605.340  567.622   -0.062 27.334
## nodefactor.region.EW    367.588  347.872   -0.054 25.706
## nodefactor.region.OW   1182.255 1171.411   -0.009 60.295
## concurrent             1384.000 1266.774   -0.085 54.394
## nodematch.race..wa.B      8.480    8.993    0.061  3.301
## nodematch.race..wa.H     51.181   42.507   -0.169  6.479
## nodematch.race..wa.O   1247.081 1261.286    0.011 48.533
## nodematch.region       1614.000 1529.288   -0.052 51.823
## absdiff.sqrt.age       1664.841 1737.859    0.044 65.692
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.574   -0.032 30.040
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers8, type="formation")

plot(dx_pers8, type="duration")

plot(dx_pers8, type="dissolution")